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<li><a class="reference internal" href="#">federatedml.param package</a><ul>
<li><a class="reference internal" href="#subpackages">Subpackages</a></li>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-federatedml.param.base_param">federatedml.param.base_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.boosting_tree_param">federatedml.param.boosting_tree_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.cross_validation_param">federatedml.param.cross_validation_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.dataio_param">federatedml.param.dataio_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.encrypt_param">federatedml.param.encrypt_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.encrypted_mode_calculation_param">federatedml.param.encrypted_mode_calculation_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.evaluation_param">federatedml.param.evaluation_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.feature_binning_param">federatedml.param.feature_binning_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.feature_selection_param">federatedml.param.feature_selection_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.ftl_param">federatedml.param.ftl_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.intersect_param">federatedml.param.intersect_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.logistic_regression_param">federatedml.param.logistic_regression_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.one_vs_rest_param">federatedml.param.one_vs_rest_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.onehot_encoder_param">federatedml.param.onehot_encoder_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.param">federatedml.param.param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.predict_param">federatedml.param.predict_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.sample_param">federatedml.param.sample_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param.scale_param">federatedml.param.scale_param module</a></li>
<li><a class="reference internal" href="#module-federatedml.param">Module contents</a></li>
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  <div class="section" id="federatedml-param-package">
<h1>federatedml.param package<a class="headerlink" href="#federatedml-param-package" title="Permalink to this headline">¶</a></h1>
<div class="section" id="subpackages">
<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this headline">¶</a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="federatedml.param.test.html">federatedml.param.test package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="federatedml.param.test.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="federatedml.param.test.html#module-federatedml.param.test.param_json_test">federatedml.param.test.param_json_test module</a></li>
<li class="toctree-l2"><a class="reference internal" href="federatedml.param.test.html#module-federatedml.param.test">Module contents</a></li>
</ul>
</li>
</ul>
</div>
</div>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="module-federatedml.param.base_param">
<span id="federatedml-param-base-param-module"></span><h2>federatedml.param.base_param module<a class="headerlink" href="#module-federatedml.param.base_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.base_param.BaseParam">
<em class="property">class </em><code class="descclassname">federatedml.param.base_param.</code><code class="descname">BaseParam</code><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<dl class="method">
<dt id="federatedml.param.base_param.BaseParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_and_change_lower">
<em class="property">static </em><code class="descname">check_and_change_lower</code><span class="sig-paren">(</span><em>param</em>, <em>valid_list</em>, <em>descr=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_and_change_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_and_change_lower" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_boolean">
<em class="property">static </em><code class="descname">check_boolean</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_boolean"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_boolean" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_decimal_float">
<em class="property">static </em><code class="descname">check_decimal_float</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_decimal_float"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_decimal_float" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_defined_type">
<em class="property">static </em><code class="descname">check_defined_type</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em>, <em>types</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_defined_type"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_defined_type" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_open_unit_interval">
<em class="property">static </em><code class="descname">check_open_unit_interval</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_open_unit_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_open_unit_interval" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_positive_integer">
<em class="property">static </em><code class="descname">check_positive_integer</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_positive_integer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_positive_integer" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_positive_number">
<em class="property">static </em><code class="descname">check_positive_number</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_positive_number"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_positive_number" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_string">
<em class="property">static </em><code class="descname">check_string</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_string"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_string" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="staticmethod">
<dt id="federatedml.param.base_param.BaseParam.check_valid_value">
<em class="property">static </em><code class="descname">check_valid_value</code><span class="sig-paren">(</span><em>param</em>, <em>descr</em>, <em>valid_values</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.check_valid_value"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.check_valid_value" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="method">
<dt id="federatedml.param.base_param.BaseParam.validate">
<code class="descname">validate</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/base_param.html#BaseParam.validate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.base_param.BaseParam.validate" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.boosting_tree_param">
<span id="federatedml-param-boosting-tree-param-module"></span><h2>federatedml.param.boosting_tree_param module<a class="headerlink" href="#module-federatedml.param.boosting_tree_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.boosting_tree_param.BoostingTreeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.boosting_tree_param.</code><code class="descname">BoostingTreeParam</code><span class="sig-paren">(</span><em>tree_param=&lt;federatedml.param.boosting_tree_param.DecisionTreeParam object&gt;</em>, <em>task_type='classification'</em>, <em>objective_param=&lt;federatedml.param.boosting_tree_param.ObjectiveParam object&gt;</em>, <em>learning_rate=0.3</em>, <em>num_trees=5</em>, <em>subsample_feature_rate=0.8</em>, <em>n_iter_no_change=True</em>, <em>tol=0.0001</em>, <em>encrypt_param=&lt;federatedml.param.encrypt_param.EncryptParam object&gt;</em>, <em>quantile_method='bin_by_sample_data'</em>, <em>bin_num=32</em>, <em>bin_gap=0.001</em>, <em>bin_sample_num=10000</em>, <em>encrypted_mode_calculator_param=&lt;federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object&gt;</em>, <em>predict_param=&lt;federatedml.param.predict_param.PredictParam object&gt;</em>, <em>cv_param=&lt;federatedml.param.cross_validation_param.CrossValidationParam object&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#BoostingTreeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.boosting_tree_param.BoostingTreeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define boosting tree parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>task_type</strong> (<em>str</em><em>, </em><em>accepted 'classification'</em><em>, </em><em>'regression' only</em><em>, </em><em>default: 'classification'</em>) – </li>
<li><strong>tree_param</strong> (<em>DecisionTreeParam Object</em><em>, </em><em>default: DecisionTreeParam</em><em>(</em><em>)</em>) – </li>
<li><strong>objective_param</strong> (<em>ObjectiveParam Object</em><em>, </em><em>default: ObjectiveParam</em><em>(</em><em>)</em>) – </li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>accepted float</em><em>, </em><em>int</em><em> or </em><em>long only</em><em>, </em><em>the learning rate of secure boost. default: 0.3</em>) – </li>
<li><strong>num_trees</strong> (<em>int</em><em>, </em><em>accepted int</em><em>, </em><em>float only</em><em>, </em><em>the max number of trees to build. default: 5</em>) – </li>
<li><strong>subsample_feature_rate</strong> (<em>float</em><em>, </em><em>a float-number in</em><em> [</em><em>0</em><em>, </em><em>1</em><em>]</em><em>, </em><em>default: 0.8</em>) – </li>
<li><strong>n_iter_no_change</strong> (<em>bool</em><em>,</em>) – when True and residual error less than tol, tree building process will stop. default: True</li>
<li><strong>encrypt_param</strong> (<em>EncodeParam Object</em><em>, </em><em>encrypt method use in secure boost</em><em>, </em><em>default: EncryptParam</em><em>(</em><em>)</em>) – </li>
<li><strong>quantile_method</strong> (<em>str</em><em>, </em><em>accepted 'bin_by_sample_data'</em><em> or </em><em>'bin_by_data_block' only</em><em>,</em>) – the quantile method use in secureboost, default: ‘bin_by_sample_data’</li>
<li><strong>bin_num</strong> (<em>int</em><em>, </em><em>positive integer greater than 1</em><em>, </em><em>bin number use in quantile. default: 32</em>) – </li>
<li><strong>bin_gap</strong> (<em>float</em><em>, </em><em>least difference between bin points</em><em>, </em><em>default: 1e-3</em>) – </li>
<li><strong>bin_sample_num</strong> (<em>int</em><em>, </em><em>if quantile method is 'bin_by_sample_data'</em><em>, </em><em>max amount of samples to find bins.</em>) – default: 10000</li>
<li><strong>encrypted_mode_calculator_param</strong> (<em>EncryptedModeCalculatorParam object</em><em>, </em><em>the calculation mode use in secureboost</em><em>,</em>) – default: EncryptedModeCalculatorParam()</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.boosting_tree_param.BoostingTreeParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#BoostingTreeParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.boosting_tree_param.BoostingTreeParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.boosting_tree_param.DecisionTreeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.boosting_tree_param.</code><code class="descname">DecisionTreeParam</code><span class="sig-paren">(</span><em>criterion_method='xgboost', criterion_params=[0.1], max_depth=5, min_sample_split=2, min_imputiry_split=0.001, min_leaf_node=1, max_split_nodes=65536, feature_importance_type='split', n_iter_no_change=True, tol=0.001</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#DecisionTreeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.boosting_tree_param.DecisionTreeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define decision tree parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>criterion_method</strong> (<em>str</em><em>, </em><em>accepted &quot;xgboost&quot; only</em><em>, </em><em>the criterion function to use</em><em>, </em><em>default: 'xgboost'</em>) – </li>
<li><strong>criterion_params</strong> (<em>list</em><em>, </em><em>should be non empty and first element is float-number</em><em>, </em><em>default: 0.1.</em>) – </li>
<li><strong>max_depth</strong> (<em>int</em><em>, </em><em>positive integer</em><em>, </em><em>the max depth of a decision tree</em><em>, </em><em>default: 5</em>) – </li>
<li><strong>min_sample_split</strong> (<em>int</em><em>, </em><em>least quantity of nodes to split</em><em>, </em><em>default: 2</em>) – </li>
<li><strong>min_impurity_split</strong> (<em>float</em><em>, </em><em>least gain of a single split need to reach</em><em>, </em><em>default: 1e-3</em>) – </li>
<li><strong>min_leaf_node</strong> (<em>int</em><em>, </em><em>when samples no more than min_leaf_node</em><em>, </em><em>it becomes a leave</em><em>, </em><em>default: 1</em>) – </li>
<li><strong>max_split_nodes</strong> (<em>int</em><em>, </em><em>positive integer</em><em>, </em><em>we will use no more than max_split_nodes to</em>) – parallel finding their splits in a batch, for memory consideration. default is 65536</li>
<li><strong>n_iter_no_change</strong> (<em>bool</em><em>, </em><em>accepted True</em><em>,</em><em>False only</em><em>, </em><em>if set to True</em><em>, </em><em>tol will use to consider</em>) – stop tree growth. default: True</li>
<li><strong>feature_importance_type</strong> (<em>str</em><em>, </em><em>support 'split'</em><em>, </em><em>'gain' only.</em>) – if is ‘split’, feature_importances calculate by feature split times,
if is ‘gain’, feature_importances calculate by feature split gain.
default: ‘split’</li>
<li><strong>tol</strong> (<em>float</em><em>, </em><em>only use when n_iter_no_change is set to True</em><em>, </em><em>default: 0.001</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.boosting_tree_param.DecisionTreeParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#DecisionTreeParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.boosting_tree_param.DecisionTreeParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.boosting_tree_param.ObjectiveParam">
<em class="property">class </em><code class="descclassname">federatedml.param.boosting_tree_param.</code><code class="descname">ObjectiveParam</code><span class="sig-paren">(</span><em>objective=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#ObjectiveParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.boosting_tree_param.ObjectiveParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define objective parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>objective</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>accepted None</em><em>,</em><em>'cross_entropy'</em><em>,</em><em>'lse'</em><em>,</em><em>'lae'</em><em>,</em><em>'log_cosh'</em><em>,</em><em>'tweedie'</em><em>,</em><em>'fair'</em><em>,</em><em>'huber' only</em><em>,</em>) – None in host’s config, should be str in guest’config.
when task_type is classification, only support cross_enctropy,
other 6 types support in regression task. default: None</li>
<li><strong>params</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>should be non empty list when objective is 'tweedie'</em><em>,</em><em>'fair'</em><em>,</em><em>'huber'</em><em>,</em>) – first element of list shoulf be a float-number large than 0.0 when objective is ‘fair’,’huber’,
first element of list should be a float-number in [1.0, 2.0) when objective is ‘tweedie’</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.boosting_tree_param.ObjectiveParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><em>task_type=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#ObjectiveParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.boosting_tree_param.ObjectiveParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.cross_validation_param">
<span id="federatedml-param-cross-validation-param-module"></span><h2>federatedml.param.cross_validation_param module<a class="headerlink" href="#module-federatedml.param.cross_validation_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.cross_validation_param.CrossValidationParam">
<em class="property">class </em><code class="descclassname">federatedml.param.cross_validation_param.</code><code class="descname">CrossValidationParam</code><span class="sig-paren">(</span><em>n_splits=5</em>, <em>mode='hetero'</em>, <em>role='guest'</em>, <em>shuffle=True</em>, <em>random_seed=1</em>, <em>evaluate_param=&lt;federatedml.param.evaluation_param.EvaluateParam object&gt;</em>, <em>need_cv=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/cross_validation_param.html#CrossValidationParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.cross_validation_param.CrossValidationParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define cross validation params</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>n_splits</strong> (<em>int</em><em>, </em><em>default: 5</em>) – Specify how many splits used in KFold</li>
<li><strong>mode</strong> (<em>str</em><em>, </em><em>default: 'Hetero'</em>) – Indicate what mode is current task</li>
<li><strong>role</strong> (<em>str</em><em>, </em><em>default: 'Guest'</em>) – Indicate what role is current party</li>
<li><strong>shuffle</strong> (<em>bool</em><em>, </em><em>default: True</em>) – Define whether do shuffle before KFold or not.</li>
<li><strong>random_seed</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Specify the random seed for numpy shuffle</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.cross_validation_param.CrossValidationParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/cross_validation_param.html#CrossValidationParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.cross_validation_param.CrossValidationParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.dataio_param">
<span id="federatedml-param-dataio-param-module"></span><h2>federatedml.param.dataio_param module<a class="headerlink" href="#module-federatedml.param.dataio_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.dataio_param.DataIOParam">
<em class="property">class </em><code class="descclassname">federatedml.param.dataio_param.</code><code class="descname">DataIOParam</code><span class="sig-paren">(</span><em>input_format='dense'</em>, <em>delimitor='</em>, <em>'</em>, <em>data_type='float64'</em>, <em>tag_with_value=False</em>, <em>tag_value_delimitor=':'</em>, <em>missing_fill=True</em>, <em>default_value=0</em>, <em>missing_fill_method=None</em>, <em>missing_impute=None</em>, <em>outlier_replace=True</em>, <em>outlier_replace_method=None</em>, <em>outlier_impute=None</em>, <em>outlier_replace_value=0</em>, <em>with_label=False</em>, <em>label_idx=0</em>, <em>label_type='int'</em>, <em>output_format='dense'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/dataio_param.html#DataIOParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.dataio_param.DataIOParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define dataio parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input_format</strong> (<em>str</em><em>, </em><em>accepted 'dense'</em><em>,</em><em>'sparse' 'tag' only in this version. default: 'dense'</em>) – </li>
<li><strong>delimitor</strong> (<em>str</em><em>, </em><em>the delimitor of data input</em><em>, </em><em>default: '</em><em>,</em><em>'</em>) – </li>
<li><strong>data_type</strong> (<em>str</em><em>, </em><em>the data type of data input</em><em>, </em><em>accedted 'float'</em><em>,</em><em>'float64'</em><em>,</em><em>'int'</em><em>,</em><em>'int64'</em><em>,</em><em>'str'</em><em>,</em><em>'long'</em>) – “default: “float64”</li>
<li><strong>tag_with_value</strong> (<em>bool</em><em>, </em><em>use if input_format is 'tag'</em><em>, </em><em>if tag_with_value is True</em><em>, </em><em>input column data format should be tag</em><em>[</em><em>delimitor</em><em>]</em><em>value</em><em>, </em><em>otherwise is tag only</em>) – </li>
<li><strong>tag_value_delimitor</strong> (<em>str</em><em>, </em><em>use if input_format is 'tag' and 'tag_with_value' is True</em><em>, </em><em>delimitor of tag</em><em>[</em><em>delimitor</em><em>]</em><em>value column value.</em>) – </li>
<li><strong>missing_fill</strong> (<em>bool</em><em>, </em><em>need to fill missing value</em><em> or </em><em>not</em><em>, </em><em>accepted only True/False</em><em>, </em><em>default: True</em>) – </li>
<li><strong>default_value</strong> (<em>None</em><em> or </em><em>single object type</em><em> or </em><em>list</em><em>, </em><em>the value to replace missing value.</em>) – <p>if None, it will use default value define in federatedml/feature/imputer.py,
if single object, will fill missing value with this object,
if list, it’s length should be the sample of input data’ feature dimension,</p>
<blockquote>
<div>means that if some column happens to have missing values, it will replace it
the value by element in the identical position of this list.</div></blockquote>
<p>default: None</p>
</li>
<li><strong>missing_fill_method</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>the method to replace missing value</em><em>, </em><em>should be one of</em><em> [</em><em>None</em><em>, </em><em>'min'</em><em>, </em><em>'max'</em><em>, </em><em>'mean'</em><em>, </em><em>'designated'</em><em>]</em><em>, </em><em>default: None</em>) – </li>
<li><strong>missing_impute</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>element of list can be any type</em><em>, or </em><em>auto generated if value is None</em><em>, </em><em>define which values to be consider as missing</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_replace</strong> (<em>bool</em><em>, </em><em>need to replace outlier value</em><em> or </em><em>not</em><em>, </em><em>accepted only True/False</em><em>, </em><em>default: True</em>) – </li>
<li><strong>outlier_replace_method</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>the method to replace missing value</em><em>, </em><em>should be one of</em><em> [</em><em>None</em><em>, </em><em>'min'</em><em>, </em><em>'max'</em><em>, </em><em>'mean'</em><em>, </em><em>'designated'</em><em>]</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_impute</strong> (<em>None</em><em> or </em><em>list</em><em>,  </em><em>element of list can be any type</em><em>, </em><em>which values should be regard as missing value</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_replace_value</strong> (<em>None</em><em> or </em><em>single object type</em><em> or </em><em>list</em><em>, </em><em>the value to replace outlier.</em>) – <p>if None, it will use default value define in federatedml/feature/imputer.py,
if single object, will replace outlier with this object,
if list, it’s length should be the sample of input data’ feature dimension,</p>
<blockquote>
<div>means that if some column happens to have outliers, it will replace it
the value by element in the identical position of this list.</div></blockquote>
<p>default: None</p>
</li>
<li><strong>with_label</strong> (<em>bool</em><em>, </em><em>True if input data consist of label</em><em>, </em><em>False otherwise. default: 'false'</em>) – </li>
<li><strong>label_idx</strong> (<em>int</em><em>, </em><em>accepted 'int'</em><em>,</em><em>'long' only</em><em>, </em><em>use when with_label is True. default: 'false'</em>) – </li>
<li><strong>label_type</strong> (<em>object</em><em>, </em><em>accepted 'int'</em><em>,</em><em>'int64'</em><em>,</em><em>'float'</em><em>,</em><em>'float64'</em><em>,</em><em>'long'</em><em>,</em><em>'str' only</em><em>,</em>) – use when with_label is True. default: ‘false’</li>
<li><strong>output_format</strong> (<em>str</em><em>, </em><em>accepted 'dense'</em><em>,</em><em>'sparse' only in this version. default: 'dense'</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.dataio_param.DataIOParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/dataio_param.html#DataIOParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.dataio_param.DataIOParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.encrypt_param">
<span id="federatedml-param-encrypt-param-module"></span><h2>federatedml.param.encrypt_param module<a class="headerlink" href="#module-federatedml.param.encrypt_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.encrypt_param.EncryptParam">
<em class="property">class </em><code class="descclassname">federatedml.param.encrypt_param.</code><code class="descname">EncryptParam</code><span class="sig-paren">(</span><em>method='Paillier'</em>, <em>key_length=1024</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/encrypt_param.html#EncryptParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.encrypt_param.EncryptParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define encryption method that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>default: 'Paillier'</em>) – If method is ‘Paillier’, Paillier encryption will be used for federated ml.
To use non-encryption version in HomoLR, just set this parameter to be any other str.
For detail of Paillier encryption, please check out the paper mentioned in README file.</li>
<li><strong>key_length</strong> (<em>int</em><em>, </em><em>default: 1024</em>) – Used to specify the length of key in this encryption method. Only needed when method is ‘Paillier’</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.encrypt_param.EncryptParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/encrypt_param.html#EncryptParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.encrypt_param.EncryptParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.encrypted_mode_calculation_param">
<span id="federatedml-param-encrypted-mode-calculation-param-module"></span><h2>federatedml.param.encrypted_mode_calculation_param module<a class="headerlink" href="#module-federatedml.param.encrypted_mode_calculation_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam">
<em class="property">class </em><code class="descclassname">federatedml.param.encrypted_mode_calculation_param.</code><code class="descname">EncryptedModeCalculatorParam</code><span class="sig-paren">(</span><em>mode='strict'</em>, <em>re_encrypted_rate=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/encrypted_mode_calculation_param.html#EncryptedModeCalculatorParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the encrypted_mode_calulator parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>mode</strong> (<em>str</em><em>, </em><em>support 'strict'</em><em>, </em><em>'fast'</em><em>, </em><em>'balance' only</em><em>, </em><em>default: strict</em>) – </li>
<li><strong>re_encrypted_rate</strong> (<em>float</em><em> or </em><em>int</em><em>, </em><em>numeric number</em><em>, </em><em>use when mode equals to 'strict'</em><em>, </em><em>defualt: 1</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/encrypted_mode_calculation_param.html#EncryptedModeCalculatorParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.evaluation_param">
<span id="federatedml-param-evaluation-param-module"></span><h2>federatedml.param.evaluation_param module<a class="headerlink" href="#module-federatedml.param.evaluation_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.evaluation_param.EvaluateParam">
<em class="property">class </em><code class="descclassname">federatedml.param.evaluation_param.</code><code class="descname">EvaluateParam</code><span class="sig-paren">(</span><em>eval_type='binary'</em>, <em>pos_label=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/evaluation_param.html#EvaluateParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.evaluation_param.EvaluateParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the evaluation method of binary/multiple classification and regression</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>metrics</strong> (<em>A list of evaluate index. Support 'auc'</em><em>, </em><em>'ks'</em><em>, </em><em>'lift'</em><em>, </em><em>'precision'</em><em> ,</em><em>'recall' and 'accuracy'</em><em>, </em><em>'explain_variance'</em><em>,</em>) – ‘mean_absolute_error’, ‘mean_squared_error’, ‘mean_squared_log_error’,’median_absolute_error’,’r2_score’,’root_mean_squared_error’.
For example, metrics can be set as [‘auc’, ‘precision’, ‘recall’], then the results of these indexes will be output.</li>
<li><strong>eval_type</strong> (<em>string</em><em>, </em><em>support 'binary' for HomoLR</em><em>, </em><em>HeteroLR and Secureboosting. support 'regression' for Secureboosting. 'multi' is not support these version</em>) – </li>
<li><strong>pos_label</strong> (<em>specify positive label type</em><em>, </em><em>can be int</em><em>, </em><em>float and str</em><em>, </em><em>this depend on the data's label</em><em>, </em><em>this parameter effective only for 'binary'</em>) – </li>
<li><strong>thresholds</strong> (<em>A list of threshold. Specify the threshold use to separate positive and negative class. for example</em><em> [</em><em>0.1</em><em>, </em><em>0.3</em><em>,</em><em>0.5</em><em>]</em><em>, </em><em>this parameter effective only for 'binary'</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.evaluation_param.EvaluateParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/evaluation_param.html#EvaluateParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.evaluation_param.EvaluateParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.feature_binning_param">
<span id="federatedml-param-feature-binning-param-module"></span><h2>federatedml.param.feature_binning_param module<a class="headerlink" href="#module-federatedml.param.feature_binning_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.feature_binning_param.FeatureBinningParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_binning_param.</code><code class="descname">FeatureBinningParam</code><span class="sig-paren">(</span><em>method='quantile'</em>, <em>compress_thres=10000</em>, <em>head_size=10000</em>, <em>error=0.001</em>, <em>bin_num=10</em>, <em>cols=-1</em>, <em>adjustment_factor=0.5</em>, <em>transform_param=&lt;federatedml.param.feature_binning_param.TransformParam object&gt;</em>, <em>local_only=False</em>, <em>need_run=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_binning_param.html#FeatureBinningParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_binning_param.FeatureBinningParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the feature binning method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>'quantile'</em><em> or </em><em>'bucket'</em><em>, </em><em>default: 'quantile'</em>) – Binning method.</li>
<li><strong>compress_thres</strong> (<em>int</em><em>, </em><em>default: 10000</em>) – When the number of saved summaries exceed this threshold, it will call its compress function</li>
<li><strong>head_size</strong> (<em>int</em><em>, </em><em>default: 10000</em>) – The buffer size to store inserted observations. When head list reach this buffer size, the
QuantileSummaries object start to generate summary(or stats) and insert into its sampled list.</li>
<li><strong>error</strong> (<em>float</em><em>, </em><em>0 &lt;= error &lt; 1 default: 0.001</em>) – The error of tolerance of binning. The final split point comes from original data, and the rank
of this value is close to the exact rank. More precisely,
floor((p - 2 * error) * N) &lt;= rank(x) &lt;= ceil((p + 2 * error) * N)
where p is the quantile in float, and N is total number of data.</li>
<li><strong>bin_num</strong> (<em>int</em><em>, </em><em>bin_num &gt; 0</em><em>, </em><em>default: 10</em>) – The max bin number for binning</li>
<li><strong>cols</strong> (<em>list of int</em><em> or </em><em>int</em><em>, </em><em>default: -1</em>) – Specify which columns need to calculated. -1 represent for all columns. If you need to indicate specific
cols, provide a list of header index instead of -1.</li>
<li><strong>adjustment_factor</strong> (<em>float</em><em>, </em><em>default: 0.5</em>) – the adjustment factor when calculating WOE. This is useful when there is no event or non-event in
a bin.</li>
<li><strong>local_only</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether just provide binning method to guest party. If true, host party will do nothing.</li>
<li><strong>transform_param</strong> (<a class="reference internal" href="#federatedml.param.feature_binning_param.TransformParam" title="federatedml.param.feature_binning_param.TransformParam"><em>TransformParam</em></a>) – Define how to transfer the binned data.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_binning_param.FeatureBinningParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_binning_param.html#FeatureBinningParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_binning_param.FeatureBinningParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.feature_binning_param.TransformParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_binning_param.</code><code class="descname">TransformParam</code><span class="sig-paren">(</span><em>transform_cols=-1</em>, <em>transform_type='bin_num'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_binning_param.html#TransformParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_binning_param.TransformParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define how to transfer the cols</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>transform_cols</strong> (<em>list of column index</em><em>, </em><em>default: -1</em>) – Specify which columns need to be transform. If column index is None, None of columns will be transformed.
If it is -1, it will use same columns as cols in binning module.</li>
<li><strong>transform_type</strong> (<em>str</em><em>, </em><em>'bin_num'or None default: None</em>) – Specify which value these columns going to replace. If it is set as None, nothing will be replaced.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_binning_param.TransformParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_binning_param.html#TransformParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_binning_param.TransformParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.feature_selection_param">
<span id="federatedml-param-feature-selection-param-module"></span><h2>federatedml.param.feature_selection_param module<a class="headerlink" href="#module-federatedml.param.feature_selection_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.feature_selection_param.FeatureSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_selection_param.</code><code class="descname">FeatureSelectionParam</code><span class="sig-paren">(</span><em>select_cols=-1</em>, <em>filter_methods=None</em>, <em>local_only=False</em>, <em>unique_param=&lt;federatedml.param.feature_selection_param.UniqueValueParam object&gt;</em>, <em>iv_value_param=&lt;federatedml.param.feature_selection_param.IVValueSelectionParam object&gt;</em>, <em>iv_percentile_param=&lt;federatedml.param.feature_selection_param.IVPercentileSelectionParam object&gt;</em>, <em>variance_coe_param=&lt;federatedml.param.feature_selection_param.VarianceOfCoeSelectionParam object&gt;</em>, <em>outlier_param=&lt;federatedml.param.feature_selection_param.OutlierColsSelectionParam object&gt;</em>, <em>need_run=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#FeatureSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.FeatureSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the feature selection parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>select_cols</strong> (<em>list</em><em> or </em><em>int</em><em>, </em><em>default: -1</em>) – Specify which columns need to calculated. -1 represent for all columns.</li>
<li><strong>filter_methods</strong> (<em>list</em><em>, </em><em>[</em><em>&quot;unique_value&quot;</em><em>, </em><em>&quot;iv_value_thres&quot;</em><em>, </em><em>&quot;iv_percentile&quot;</em><em>,</em>) – <blockquote>
<div><dl class="docutils">
<dt>“coefficient_of_variation_value_thres”, “outlier_cols”],</dt>
<dd>default: [“unique_value”]</dd>
</dl>
</div></blockquote>
<p>Specify the filter methods used in feature selection. The orders of filter used is depended on this list.
Please be notified that, if a percentile method is used after some certain filter method,
the percentile represent for the ratio of rest features.</p>
<p>e.g. If you have 10 features at the beginning. After first filter method, you have 8 rest. Then, you want
top 80% highest iv feature. Here, we will choose floor(0.8 * 8) = 6 features instead of 8.</p>
<p>unique_value: filter the columns if all values in this feature is the same</p>
<dl class="docutils">
<dt>iv_value_thres: Use information value to filter columns. If this method is set, a float threshold need to be provided.</dt>
<dd>Filter those columns whose iv is smaller than threshold.</dd>
<dt>iv_percentile: Use information value to filter columns. If this method is set, a float ratio threshold</dt>
<dd>need to be provided. Pick floor(ratio * feature_num) features with higher iv. If multiple features around
the threshold are same, all those columns will be keep.</dd>
</dl>
<p>coefficient_of_variation_value_thres: Use coefficient of variation to judge whether filtered or not.</p>
<p>outlier_cols: Filter columns whose certain percentile value is larger than a threshold.</p>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_selection_param.FeatureSelectionParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#FeatureSelectionParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.FeatureSelectionParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.feature_selection_param.IVPercentileSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_selection_param.</code><code class="descname">IVPercentileSelectionParam</code><span class="sig-paren">(</span><em>percentile_threshold=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#IVPercentileSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.IVPercentileSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Use information values to select features.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>percentile_threshold</strong> (<em>float</em><em>, </em><em>0 &lt;= percentile_threshold &lt;= 1.0</em><em>, </em><em>default: 1.0</em>) – Percentile threshold for iv_percentile method</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_selection_param.IVPercentileSelectionParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#IVPercentileSelectionParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.IVPercentileSelectionParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.feature_selection_param.IVValueSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_selection_param.</code><code class="descname">IVValueSelectionParam</code><span class="sig-paren">(</span><em>value_threshold=0.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#IVValueSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.IVValueSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Use information values to select features.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>value_threshold</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Used if iv_value_thres method is used in feature selection.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_selection_param.IVValueSelectionParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#IVValueSelectionParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.IVValueSelectionParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.feature_selection_param.OutlierColsSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_selection_param.</code><code class="descname">OutlierColsSelectionParam</code><span class="sig-paren">(</span><em>percentile=1.0</em>, <em>upper_threshold=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#OutlierColsSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.OutlierColsSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Given percentile and threshold. Judge if this quantile point is larger than threshold. Filter those larger ones.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>percentile</strong> (<em>float</em><em>, </em><em>[</em><em>0.</em><em>, </em><em>1.</em><em>] </em><em>default: 1.0</em>) – The percentile points to compare.</li>
<li><strong>upper_threshold</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Percentile threshold for coefficient_of_variation_percentile method</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_selection_param.OutlierColsSelectionParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#OutlierColsSelectionParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.OutlierColsSelectionParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.feature_selection_param.UniqueValueParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_selection_param.</code><code class="descname">UniqueValueParam</code><span class="sig-paren">(</span><em>eps=1e-05</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#UniqueValueParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.UniqueValueParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Use the difference between max-value and min-value to judge.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>eps</strong> (<em>float</em><em>, </em><em>default: 1e-5</em>) – The column(s) will be filtered if its difference is smaller than eps.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_selection_param.UniqueValueParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#UniqueValueParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.UniqueValueParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.feature_selection_param.VarianceOfCoeSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.feature_selection_param.</code><code class="descname">VarianceOfCoeSelectionParam</code><span class="sig-paren">(</span><em>value_threshold=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#VarianceOfCoeSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.VarianceOfCoeSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Use coefficient of variation to select features. When judging, the absolute value will be used.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>value_threshold</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Used if coefficient_of_variation_value_thres method is used in feature selection. Filter those
columns who has smaller coefficient of variance than the threshold.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.feature_selection_param.VarianceOfCoeSelectionParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_selection_param.html#VarianceOfCoeSelectionParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.feature_selection_param.VarianceOfCoeSelectionParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.ftl_param">
<span id="federatedml-param-ftl-param-module"></span><h2>federatedml.param.ftl_param module<a class="headerlink" href="#module-federatedml.param.ftl_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.ftl_param.FTLDataParam">
<em class="property">class </em><code class="descclassname">federatedml.param.ftl_param.</code><code class="descname">FTLDataParam</code><span class="sig-paren">(</span><em>file_path=None</em>, <em>n_feature_guest=10</em>, <em>n_feature_host=23</em>, <em>overlap_ratio=0.1</em>, <em>guest_split_ratio=0.9</em>, <em>num_samples=None</em>, <em>balanced=True</em>, <em>is_read_table=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#FTLDataParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.FTLDataParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Defines parameters for FTL data model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>file_path</strong> (<em>str</em><em>, </em><em>default: None</em>) – The file path to FTL data configuration JSON file, must be string or None</li>
<li><strong>n_feature_guest</strong> (<em>integer</em><em>, </em><em>default: 10</em>) – The number of features at guest side, must be positive integer</li>
<li><strong>n_feature_host</strong> (<em>integer</em><em>, </em><em>default: 23</em>) – The number of features at host side, must be positive integer</li>
<li><strong>overlap_ratio</strong> (<em>float</em><em>, </em><em>default: 0.1</em>) – The ratio of overlapping samples between guest and host, must between 0 and 1 exclusively</li>
<li><strong>guest_split_ratio</strong> (<em>float</em><em>, </em><em>default: 0.9</em>) – The ratio of number of samples excluding overlapping samples at guest side, must between 0 and 1 exclusively</li>
<li><strong>num_samples</strong> (<em>numeric</em><em>, </em><em>default: None</em>) – The total number of samples used for train/validation/test, must be positive integer or None. If None, all samples
would be used.</li>
<li><strong>balanced</strong> (<em>bool</em><em>, </em><em>default; True</em>) – The indicator indicating whether balance samples, must be bool</li>
<li><strong>is_read_table</strong> (<em>bool</em><em>, </em><em>default; False</em>) – The indicator indicating whether read data from dtable, must be bool</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.ftl_param.FTLDataParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#FTLDataParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.FTLDataParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.ftl_param.FTLModelParam">
<em class="property">class </em><code class="descclassname">federatedml.param.ftl_param.</code><code class="descname">FTLModelParam</code><span class="sig-paren">(</span><em>max_iteration=10</em>, <em>batch_size=64</em>, <em>eps=1e-05</em>, <em>alpha=100</em>, <em>lr_decay=0.001</em>, <em>l2_para=1</em>, <em>is_encrypt=True</em>, <em>enc_ftl='dct_enc_ftl'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#FTLModelParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.FTLModelParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Defines parameters for FTL model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>max_iteration</strong> (<em>integer</em><em>, </em><em>default: 10</em>) – The number of passes over the training data (aka epochs), must be positive integer</li>
<li><strong>eps</strong> (<em>numeric</em><em>, </em><em>default: 1e-3</em>) – The converge threshold, must be positive number</li>
<li><strong>alpha</strong> (<em>numeric</em><em>, </em><em>default: 100</em>) – The weight for objective function loss, must be positive number</li>
<li><strong>is_encrypt</strong> (<em>bool</em><em>, </em><em>default; True</em>) – The indicator indicating whether we use encrypted version of ftl or plain version, must be bool</li>
<li><strong>enc_ftl</strong> (<em>str default &quot;dct_enc_ftl&quot;</em>) – The name for encrypted federated transfer learning algorithm</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.ftl_param.FTLModelParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#FTLModelParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.FTLModelParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.ftl_param.FTLValidDataParam">
<em class="property">class </em><code class="descclassname">federatedml.param.ftl_param.</code><code class="descname">FTLValidDataParam</code><span class="sig-paren">(</span><em>file_path=None</em>, <em>num_samples=None</em>, <em>is_read_table=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#FTLValidDataParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.FTLValidDataParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Defines parameters for FTL validation data model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>file_path</strong> (<em>str</em><em>, </em><em>default: None</em>) – The file path to FTL data configuration JSON file, must be string or None</li>
<li><strong>num_samples</strong> (<em>numeric</em><em>, </em><em>default: None</em>) – The total number of samples used for validation, must be positive integer or None. If None, all samples
would be used.</li>
<li><strong>is_read_table</strong> (<em>bool</em><em>, </em><em>default; False</em>) – The indicator indicating whether read data from dtable, must be bool</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.ftl_param.FTLValidDataParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#FTLValidDataParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.FTLValidDataParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.ftl_param.LocalModelParam">
<em class="property">class </em><code class="descclassname">federatedml.param.ftl_param.</code><code class="descname">LocalModelParam</code><span class="sig-paren">(</span><em>input_dim=None</em>, <em>encode_dim=5</em>, <em>learning_rate=0.001</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#LocalModelParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.LocalModelParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Defines parameters for FTL model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input_dim</strong> (<em>integer</em><em>, </em><em>default: None</em>) – The dimension of input samples, must be positive integer</li>
<li><strong>encode_dim</strong> (<em>integer</em><em>, </em><em>default: 5</em>) – The dimension of the encoded representation of input samples, must be positive integer</li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>default: 0.001</em>) – The learning rate for training model, must between 0 and 1 exclusively</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.ftl_param.LocalModelParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/ftl_param.html#LocalModelParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ftl_param.LocalModelParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.intersect_param">
<span id="federatedml-param-intersect-param-module"></span><h2>federatedml.param.intersect_param module<a class="headerlink" href="#module-federatedml.param.intersect_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.intersect_param.EncodeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.intersect_param.</code><code class="descname">EncodeParam</code><span class="sig-paren">(</span><em>salt=''</em>, <em>encode_method='none'</em>, <em>base64=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/intersect_param.html#EncodeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.intersect_param.EncodeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the encode method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>salt</strong> (<em>the src data string will be str = str + salt</em><em>, </em><em>default by empty string</em>) – </li>
<li><strong>encode_method</strong> (<em>str</em><em>, </em><em>the encode method of src data string</em><em>, </em><em>it support md5</em><em>, </em><em>sha1</em><em>, </em><em>sha224</em><em>, </em><em>sha256</em><em>, </em><em>sha384</em><em>, </em><em>sha512</em><em>, </em><em>default by None</em>) – </li>
<li><strong>base64</strong> (<em>bool</em><em>, </em><em>if True</em><em>, </em><em>the result of encode will be changed to base64</em><em>, </em><em>default by False</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.intersect_param.EncodeParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/intersect_param.html#EncodeParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.intersect_param.EncodeParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.intersect_param.IntersectParam">
<em class="property">class </em><code class="descclassname">federatedml.param.intersect_param.</code><code class="descname">IntersectParam</code><span class="sig-paren">(</span><em>intersect_method='raw'</em>, <em>random_bit=128</em>, <em>is_send_intersect_ids=True</em>, <em>is_get_intersect_ids=True</em>, <em>join_role='guest'</em>, <em>with_encode=False</em>, <em>encode_params=&lt;federatedml.param.intersect_param.EncodeParam object&gt;</em>, <em>only_output_key=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/intersect_param.html#IntersectParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.intersect_param.IntersectParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the intersect method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>intersect_method</strong> (<em>str</em><em>, </em><em>it supports 'rsa' and 'raw'</em><em>, </em><em>default by 'raw'</em>) – </li>
<li><strong>random_bit</strong> (<em>positive int</em><em>, </em><em>it will define the encrypt length of rsa algorithm. It effective only for intersect_method is rsa</em>) – </li>
<li><strong>is_send_intersect_ids</strong> (<em>bool. In rsa</em><em>, </em><em>'is_send_intersect_ids' is True means guest will send intersect results to host</em><em>, </em><em>and False will not.</em>) – while in raw, ‘is_send_intersect_ids’ is True means the role of “join_role” will send intersect results and the other will get them.
Default by True.</li>
<li><strong>is_get_intersect_ids</strong> (<em>bool</em><em>, </em><em>In rsa</em><em>, </em><em>it will get the results from other. It effective only for rsa and only be True will other's 'is_send_intersect_ids' is True.Default by True</em>) – </li>
<li><strong>join_role</strong> (<em>str</em><em>, </em><em>it supports &quot;guest&quot; and &quot;host&quot; only and effective only for raw. If it is &quot;guest&quot;</em><em>, </em><em>the host will send its ids to guest and find the intersection of</em>) – ids in guest; if it is “host”, the guest will send its ids. Default by “guest”.</li>
<li><strong>with_encode</strong> (<em>bool</em><em>, </em><em>if True</em><em>, </em><em>it will use encode method for intersect ids. It effective only for &quot;raw&quot;.</em>) – </li>
<li><strong>encode_params</strong> (<a class="reference internal" href="#federatedml.param.intersect_param.EncodeParam" title="federatedml.param.intersect_param.EncodeParam"><em>EncodeParam</em></a><em>, </em><em>it effective only for with_encode is True</em>) – </li>
<li><strong>only_output_key</strong> (<em>bool</em><em>, </em><em>if true</em><em>, </em><em>the results of intersection will include key and value which from input data; if false</em><em>, </em><em>it will just include key from input</em>) – data and the value will be empty or some useless character like “intersect_id”</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.intersect_param.IntersectParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/intersect_param.html#IntersectParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.intersect_param.IntersectParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.logistic_regression_param">
<span id="federatedml-param-logistic-regression-param-module"></span><h2>federatedml.param.logistic_regression_param module<a class="headerlink" href="#module-federatedml.param.logistic_regression_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.logistic_regression_param.InitParam">
<em class="property">class </em><code class="descclassname">federatedml.param.logistic_regression_param.</code><code class="descname">InitParam</code><span class="sig-paren">(</span><em>init_method='random_uniform'</em>, <em>init_const=1</em>, <em>fit_intercept=True</em>, <em>random_seed=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#InitParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.logistic_regression_param.InitParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Initialize Parameters used in initializing a model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>init_method</strong> (<em>str</em><em>, </em><em>'random_uniform'</em><em>, </em><em>'random_normal'</em><em>, </em><em>'ones'</em><em>, </em><em>'zeros'</em><em> or </em><em>'const'. default: 'random_uniform'</em>) – Initial method.</li>
<li><strong>init_const</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default: 1</em>) – Required when init_method is ‘const’. Specify the constant.</li>
<li><strong>fit_intercept</strong> (<em>bool</em><em>, </em><em>default: True</em>) – Whether to initialize the intercept or not.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.logistic_regression_param.InitParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#InitParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.logistic_regression_param.InitParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.logistic_regression_param.LogisticParam">
<em class="property">class </em><code class="descclassname">federatedml.param.logistic_regression_param.</code><code class="descname">LogisticParam</code><span class="sig-paren">(</span><em>penalty='L2'</em>, <em>eps=1e-05</em>, <em>alpha=1.0</em>, <em>optimizer='sgd'</em>, <em>party_weight=1</em>, <em>batch_size=-1</em>, <em>learning_rate=0.01</em>, <em>init_param=&lt;federatedml.param.logistic_regression_param.InitParam object&gt;</em>, <em>max_iter=100</em>, <em>converge_func='diff'</em>, <em>encrypt_param=&lt;federatedml.param.encrypt_param.EncryptParam object&gt;</em>, <em>re_encrypt_batches=2</em>, <em>encrypted_mode_calculator_param=&lt;federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object&gt;</em>, <em>need_run=True</em>, <em>predict_param=&lt;federatedml.param.predict_param.PredictParam object&gt;</em>, <em>cv_param=&lt;federatedml.param.cross_validation_param.CrossValidationParam object&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#LogisticParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.logistic_regression_param.LogisticParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Parameters used for Logistic Regression both for Homo mode or Hetero mode.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>penalty</strong> (<em>str</em><em>, </em><em>'L1'</em><em> or </em><em>'L2'. default: 'L2'</em>) – Penalty method used in LR. Please note that, when using encrypted version in HomoLR,
‘L1’ is not supported.</li>
<li><strong>eps</strong> (<em>float</em><em>, </em><em>default: 1e-5</em>) – The tolerance of convergence</li>
<li><strong>alpha</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Regularization strength coefficient.</li>
<li><strong>optimizer</strong> (<em>str</em><em>, </em><em>'sgd'</em><em>, </em><em>'rmsprop'</em><em>, </em><em>'adam'</em><em> or </em><em>'adagrad'</em><em>, </em><em>default: 'sgd'</em>) – Optimize method</li>
<li><strong>party_weight</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default: 1</em>) – Required in Homo LR. Setting the weight of model updated for this party.
The higher weight set, the higher influence made for this party when updating model.</li>
<li><strong>batch_size</strong> (<em>int</em><em>, </em><em>default: -1</em>) – Batch size when updating model. -1 means use all data in a batch. i.e. Not to use mini-batch strategy.</li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>default: 0.01</em>) – Learning rate</li>
<li><strong>max_iter</strong> (<em>int</em><em>, </em><em>default: 100</em>) – The maximum iteration for training.</li>
<li><strong>converge_func</strong> (<em>str</em><em>, </em><em>'diff'</em><em> or </em><em>'abs'</em><em>, </em><em>default: 'diff'</em>) – <dl class="docutils">
<dt>Method used to judge converge or not.</dt>
<dd><ol class="first last loweralpha">
<li>diff： Use difference of loss between two iterations to judge whether converge.</li>
<li>abs: Use the absolute value of loss to judge whether converge. i.e. if loss &lt; eps, it is converged.</li>
</ol>
</dd>
</dl>
</li>
<li><strong>re_encrypt_batches</strong> (<em>int</em><em>, </em><em>default: 2</em>) – Required when using encrypted version HomoLR. Since multiple batch updating coefficient may cause
overflow error. The model need to be re-encrypt for every several batches. Please be careful when setting
this parameter. Too large batches may cause training failure.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.logistic_regression_param.LogisticParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#LogisticParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.logistic_regression_param.LogisticParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.one_vs_rest_param">
<span id="federatedml-param-one-vs-rest-param-module"></span><h2>federatedml.param.one_vs_rest_param module<a class="headerlink" href="#module-federatedml.param.one_vs_rest_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.one_vs_rest_param.OneVsRestParam">
<em class="property">class </em><code class="descclassname">federatedml.param.one_vs_rest_param.</code><code class="descname">OneVsRestParam</code><span class="sig-paren">(</span><em>has_arbiter=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/one_vs_rest_param.html#OneVsRestParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.one_vs_rest_param.OneVsRestParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the one_vs_rest parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>has_arbiter</strong> (<em>bool. For some algorithm</em><em>, </em><em>may not has arbiter</em><em>, </em><em>for instances</em><em>, </em><em>secureboost of FATE</em><em>,</em>) – for these algorithms, it should be set to false.
default true</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.one_vs_rest_param.OneVsRestParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/one_vs_rest_param.html#OneVsRestParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.one_vs_rest_param.OneVsRestParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.onehot_encoder_param">
<span id="federatedml-param-onehot-encoder-param-module"></span><h2>federatedml.param.onehot_encoder_param module<a class="headerlink" href="#module-federatedml.param.onehot_encoder_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.onehot_encoder_param.OneHotEncoderParam">
<em class="property">class </em><code class="descclassname">federatedml.param.onehot_encoder_param.</code><code class="descname">OneHotEncoderParam</code><span class="sig-paren">(</span><em>cols=-1</em>, <em>need_run=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/onehot_encoder_param.html#OneHotEncoderParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.onehot_encoder_param.OneHotEncoderParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>cols</strong> (<em>list</em><em> or </em><em>int</em><em>, </em><em>default: -1</em>) – Specify which columns need to calculated. -1 represent for all columns.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.onehot_encoder_param.OneHotEncoderParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/onehot_encoder_param.html#OneHotEncoderParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.onehot_encoder_param.OneHotEncoderParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.param">
<span id="federatedml-param-param-module"></span><h2>federatedml.param.param module<a class="headerlink" href="#module-federatedml.param.param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.param.BoostingTreeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">BoostingTreeParam</code><span class="sig-paren">(</span><em>tree_param=&lt;federatedml.param.param.DecisionTreeParam object&gt;</em>, <em>task_type='classification'</em>, <em>objective_param=&lt;federatedml.param.param.ObjectiveParam object&gt;</em>, <em>learning_rate=0.3</em>, <em>num_trees=5</em>, <em>subsample_feature_rate=0.8</em>, <em>n_iter_no_change=True</em>, <em>tol=0.0001</em>, <em>encrypt_param=&lt;federatedml.param.param.EncryptParam object&gt;</em>, <em>quantile_method='bin_by_sample_data'</em>, <em>bin_num=32</em>, <em>bin_gap=0.001</em>, <em>bin_sample_num=10000</em>, <em>encrypted_mode_calculator_param=&lt;federatedml.param.param.EncryptedModeCalculatorParam object&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#BoostingTreeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.BoostingTreeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define boosting tree parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>task_type</strong> (<em>str</em><em>, </em><em>accepted 'classification'</em><em>, </em><em>'regression' only</em><em>, </em><em>default: 'classification'</em>) – </li>
<li><strong>tree_param</strong> (<em>DecisionTreeParam Object</em><em>, </em><em>default: DecisionTreeParam</em><em>(</em><em>)</em>) – </li>
<li><strong>objective_param</strong> (<em>ObjectiveParam Object</em><em>, </em><em>default: ObjectiveParam</em><em>(</em><em>)</em>) – </li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>accepted float</em><em>, </em><em>int</em><em> or </em><em>long only</em><em>, </em><em>the learning rate of secure boost. default: 0.3</em>) – </li>
<li><strong>num_trees</strong> (<em>int</em><em>, </em><em>accepted int</em><em>, </em><em>float only</em><em>, </em><em>the max number of trees to build. default: 5</em>) – </li>
<li><strong>subsample_feature_rate</strong> (<em>float</em><em>, </em><em>a float-number in</em><em> [</em><em>0</em><em>, </em><em>1</em><em>]</em><em>, </em><em>default: 0.8</em>) – </li>
<li><strong>n_iter_no_change</strong> (<em>bool</em><em>,</em>) – when True and residual error less than tol, tree building process will stop. default: True</li>
<li><strong>encrypt_param</strong> (<em>EncodeParam Object</em><em>, </em><em>encrypt method use in secure boost</em><em>, </em><em>default: EncryptParam</em><em>(</em><em>)</em>) – </li>
<li><strong>quantile_method</strong> (<em>str</em><em>, </em><em>accepted 'bin_by_sample_data'</em><em> or </em><em>'bin_by_data_block' only</em><em>,</em>) – the quantile method use in secureboost, default: ‘bin_by_sample_data’</li>
<li><strong>bin_num</strong> (<em>int</em><em>, </em><em>positive integer greater than 1</em><em>, </em><em>bin number use in quantile. default: 32</em>) – </li>
<li><strong>bin_gap</strong> (<em>float</em><em>, </em><em>least difference between bin points</em><em>, </em><em>default: 1e-3</em>) – </li>
<li><strong>bin_sample_num</strong> (<em>int</em><em>, </em><em>if quantile method is 'bin_by_sample_data'</em><em>, </em><em>max amount of samples to find bins.</em>) – default: 10000</li>
<li><strong>encrypted_mode_calculator_param</strong> (<em>EncryptedModeCalculatorParam object</em><em>, </em><em>the calculation mode use in secureboost</em><em>,</em>) – default: EncryptedModeCalculatorParam()</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.CoeffOfVarSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">CoeffOfVarSelectionParam</code><span class="sig-paren">(</span><em>value_threshold=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#CoeffOfVarSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.CoeffOfVarSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Use coefficient of variation to select features. When judging, the absolute value will be used.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>value_threshold</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Used if coefficient_of_variation_value_thres method is used in feature selection.</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.DataIOParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">DataIOParam</code><span class="sig-paren">(</span><em>input_format='dense'</em>, <em>delimitor='</em>, <em>'</em>, <em>data_type='float64'</em>, <em>tag_with_value=False</em>, <em>tag_value_delimitor=':'</em>, <em>missing_fill=True</em>, <em>default_value=0</em>, <em>missing_fill_method=None</em>, <em>missing_impute=None</em>, <em>outlier_replace=True</em>, <em>outlier_replace_method=None</em>, <em>outlier_impute=None</em>, <em>outlier_replace_value=0</em>, <em>with_label=False</em>, <em>label_idx=0</em>, <em>label_type='int'</em>, <em>output_format='dense'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#DataIOParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.DataIOParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define dataio parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input_format</strong> (<em>str</em><em>, </em><em>accepted 'dense'</em><em>,</em><em>'sparse' 'tag' only in this version. default: 'dense'</em>) – </li>
<li><strong>delimitor</strong> (<em>str</em><em>, </em><em>the delimitor of data input</em><em>, </em><em>default: '</em><em>,</em><em>'</em>) – </li>
<li><strong>data_type</strong> (<em>str</em><em>, </em><em>the data type of data input</em><em>, </em><em>accedted 'float'</em><em>,</em><em>'float64'</em><em>,</em><em>'int'</em><em>,</em><em>'int64'</em><em>,</em><em>'str'</em><em>,</em><em>'long'</em>) – “default: “float64”</li>
<li><strong>tag_with_value</strong> (<em>bool</em><em>, </em><em>use if input_format is 'tag'</em><em>, </em><em>if tag_with_value is True</em><em>, </em><em>input column data format should be tag</em><em>[</em><em>delimitor</em><em>]</em><em>value</em><em>, </em><em>otherwise is tag only</em>) – </li>
<li><strong>tag_value_delimitor</strong> (<em>str</em><em>, </em><em>use if input_format is 'tag' and 'tag_with_value' is True</em><em>, </em><em>delimitor of tag</em><em>[</em><em>delimitor</em><em>]</em><em>value column value.</em>) – </li>
<li><strong>missing_fill</strong> (<em>bool</em><em>, </em><em>need to fill missing value</em><em> or </em><em>not</em><em>, </em><em>accepted only True/False</em><em>, </em><em>default: True</em>) – </li>
<li><strong>default_value</strong> (<em>None</em><em> or </em><em>single object type</em><em> or </em><em>list</em><em>, </em><em>the value to replace missing value.</em>) – <p>if None, it will use default value define in federatedml/feature/imputer.py,
if single object, will fill missing value with this object,
if list, it’s length should be the sample of input data’ feature dimension,</p>
<blockquote>
<div>means that if some column happens to have missing values, it will replace it
the value by element in the identical position of this list.</div></blockquote>
<p>default: None</p>
</li>
<li><strong>missing_fill_method</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>the method to replace missing value</em><em>, </em><em>should be one of</em><em> [</em><em>None</em><em>, </em><em>'min'</em><em>, </em><em>'max'</em><em>, </em><em>'mean'</em><em>, </em><em>'designated'</em><em>]</em><em>, </em><em>default: None</em>) – </li>
<li><strong>missing_impute</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>element of list can be any type</em><em>, or </em><em>auto generated if value is None</em><em>, </em><em>define which values to be consider as missing</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_replace</strong> (<em>bool</em><em>, </em><em>need to replace outlier value</em><em> or </em><em>not</em><em>, </em><em>accepted only True/False</em><em>, </em><em>default: True</em>) – </li>
<li><strong>outlier_replace_method</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>the method to replace missing value</em><em>, </em><em>should be one of</em><em> [</em><em>None</em><em>, </em><em>'min'</em><em>, </em><em>'max'</em><em>, </em><em>'mean'</em><em>, </em><em>'designated'</em><em>]</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_impute</strong> (<em>None</em><em> or </em><em>list</em><em>,  </em><em>element of list can be any type</em><em>, </em><em>which values should be regard as missing value</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_replace_value</strong> (<em>None</em><em> or </em><em>single object type</em><em> or </em><em>list</em><em>, </em><em>the value to replace outlier.</em>) – <p>if None, it will use default value define in federatedml/feature/imputer.py,
if single object, will replace outlier with this object,
if list, it’s length should be the sample of input data’ feature dimension,</p>
<blockquote>
<div>means that if some column happens to have outliers, it will replace it
the value by element in the identical position of this list.</div></blockquote>
<p>default: None</p>
</li>
<li><strong>with_label</strong> (<em>bool</em><em>, </em><em>True if input data consist of label</em><em>, </em><em>False otherwise. default: 'false'</em>) – </li>
<li><strong>label_idx</strong> (<em>int</em><em>, </em><em>accepted 'int'</em><em>,</em><em>'long' only</em><em>, </em><em>use when with_label is True. default: 'false'</em>) – </li>
<li><strong>label_type</strong> (<em>object</em><em>, </em><em>accepted 'int'</em><em>,</em><em>'int64'</em><em>,</em><em>'float'</em><em>,</em><em>'float64'</em><em>,</em><em>'long'</em><em>,</em><em>'str' only</em><em>,</em>) – use when with_label is True. default: ‘false’</li>
<li><strong>output_format</strong> (<em>str</em><em>, </em><em>accepted 'dense'</em><em>,</em><em>'sparse' only in this version. default: 'dense'</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.DecisionTreeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">DecisionTreeParam</code><span class="sig-paren">(</span><em>criterion_method='xgboost', criterion_params=[0.1], max_depth=5, min_sample_split=2, min_imputiry_split=0.001, min_leaf_node=1, max_split_nodes=65536, feature_importance_type='split', n_iter_no_change=True, tol=0.001</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#DecisionTreeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.DecisionTreeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define decision tree parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>criterion_method</strong> (<em>str</em><em>, </em><em>accepted &quot;xgboost&quot; only</em><em>, </em><em>the criterion function to use</em><em>, </em><em>default: 'xgboost'</em>) – </li>
<li><strong>criterion_params</strong> (<em>list</em><em>, </em><em>should be non empty and first element is float-number</em><em>, </em><em>default: 0.1.</em>) – </li>
<li><strong>max_depth</strong> (<em>int</em><em>, </em><em>positive integer</em><em>, </em><em>the max depth of a decision tree</em><em>, </em><em>default: 5</em>) – </li>
<li><strong>min_sample_split</strong> (<em>int</em><em>, </em><em>least quantity of nodes to split</em><em>, </em><em>default: 2</em>) – </li>
<li><strong>min_impurity_split</strong> (<em>float</em><em>, </em><em>least gain of a single split need to reach</em><em>, </em><em>default: 1e-3</em>) – </li>
<li><strong>min_leaf_node</strong> (<em>int</em><em>, </em><em>when samples no more than min_leaf_node</em><em>, </em><em>it becomes a leave</em><em>, </em><em>default: 1</em>) – </li>
<li><strong>max_split_nodes</strong> (<em>int</em><em>, </em><em>positive integer</em><em>, </em><em>we will use no more than max_split_nodes to</em>) – parallel finding their splits in a batch, for memory consideration. default is 65536</li>
<li><strong>n_iter_no_change</strong> (<em>bool</em><em>, </em><em>accepted True</em><em>,</em><em>False only</em><em>, </em><em>if set to True</em><em>, </em><em>tol will use to consider</em>) – stop tree growth. default: True</li>
<li><strong>feature_importance_type</strong> (<em>str</em><em>, </em><em>support 'split'</em><em>, </em><em>'gain' only.</em>) – if is ‘split’, feature_importances calculate by feature split times,
if is ‘gain’, feature_importances calculate by feature split gain.
default: ‘split’</li>
<li><strong>tol</strong> (<em>float</em><em>, </em><em>only use when n_iter_no_change is set to True</em><em>, </em><em>default: 0.001</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.EncodeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">EncodeParam</code><span class="sig-paren">(</span><em>salt=''</em>, <em>encode_method='none'</em>, <em>base64=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#EncodeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.EncodeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the encode method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>salt</strong> (<em>the src data string will be str = str + salt</em><em>, </em><em>default by empty string</em>) – </li>
<li><strong>encode_method</strong> (<em>str</em><em>, </em><em>the encode method of src data string</em><em>, </em><em>it support md5</em><em>, </em><em>sha1</em><em>, </em><em>sha224</em><em>, </em><em>sha256</em><em>, </em><em>sha384</em><em>, </em><em>sha512</em><em>, </em><em>default by None</em>) – </li>
<li><strong>base64</strong> (<em>bool</em><em>, </em><em>if True</em><em>, </em><em>the result of encode will be changed to base64</em><em>, </em><em>default by False</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.EncryptParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">EncryptParam</code><span class="sig-paren">(</span><em>method='Paillier'</em>, <em>key_length=1024</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#EncryptParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.EncryptParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define encryption method that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>default: 'Paillier'</em>) – If method is ‘Paillier’, Paillier encryption will be used for federated ml.
To use non-encryption version in HomoLR, just set this parameter to be any other str.
For detail of Paillier encryption, please check out the paper mentioned in README file.</li>
<li><strong>key_length</strong> (<em>int</em><em>, </em><em>default: 1024</em>) – Used to specify the length of key in this encryption method. Only needed when method is ‘Paillier’</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.EncryptedModeCalculatorParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">EncryptedModeCalculatorParam</code><span class="sig-paren">(</span><em>mode='strict'</em>, <em>re_encrypted_rate=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#EncryptedModeCalculatorParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.EncryptedModeCalculatorParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the encrypted_mode_calulator parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>mode</strong> (<em>str</em><em>, </em><em>support 'strict'</em><em>, </em><em>'fast'</em><em>, </em><em>'balance' only</em><em>, </em><em>default: strict</em>) – </li>
<li><strong>re_encrypted_rate</strong> (<em>float</em><em> or </em><em>int</em><em>, </em><em>numeric number</em><em>, </em><em>use when mode equals to 'strict'</em><em>, </em><em>defualt: 1</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.EvaluateParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">EvaluateParam</code><span class="sig-paren">(</span><em>metrics=None</em>, <em>classi_type='binary'</em>, <em>pos_label=1</em>, <em>thresholds=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#EvaluateParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.EvaluateParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the evaluation method of binary/multiple classification and regression</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>metrics</strong> (<em>A list of evaluate index. Support 'auc'</em><em>, </em><em>'ks'</em><em>, </em><em>'lift'</em><em>, </em><em>'precision'</em><em> ,</em><em>'recall' and 'accuracy'</em><em>, </em><em>'explain_variance'</em><em>,</em>) – ‘mean_absolute_error’, ‘mean_squared_error’, ‘mean_squared_log_error’,’median_absolute_error’,’r2_score’,’root_mean_squared_error’.
For example, metrics can be set as [‘auc’, ‘precision’, ‘recall’], then the results of these indexes will be output.</li>
<li><strong>classi_type</strong> (<em>string</em><em>, </em><em>support 'binary' for HomoLR</em><em>, </em><em>HeteroLR and Secureboosting. support 'regression' for Secureboosting. 'multi' is not support these version</em>) – </li>
<li><strong>pos_label</strong> (<em>specify positive label type</em><em>, </em><em>can be int</em><em>, </em><em>float and str</em><em>, </em><em>this depend on the data's label</em><em>, </em><em>this parameter effective only for 'binary'</em>) – </li>
<li><strong>thresholds</strong> (<em>A list of threshold. Specify the threshold use to separate positive and negative class. for example</em><em> [</em><em>0.1</em><em>, </em><em>0.3</em><em>,</em><em>0.5</em><em>]</em><em>, </em><em>this parameter effective only for 'binary'</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.FTLDataParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">FTLDataParam</code><span class="sig-paren">(</span><em>file_path=None</em>, <em>n_feature_guest=10</em>, <em>n_feature_host=23</em>, <em>overlap_ratio=0.1</em>, <em>guest_split_ratio=0.9</em>, <em>num_samples=None</em>, <em>balanced=True</em>, <em>is_read_table=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#FTLDataParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.FTLDataParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Defines parameters for FTL data model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>file_path</strong> (<em>str</em><em>, </em><em>default: None</em>) – The file path to FTL data configuration JSON file, must be string or None</li>
<li><strong>n_feature_guest</strong> (<em>integer</em><em>, </em><em>default: 10</em>) – The number of features at guest side, must be positive integer</li>
<li><strong>n_feature_host</strong> (<em>integer</em><em>, </em><em>default: 23</em>) – The number of features at host side, must be positive integer</li>
<li><strong>overlap_ratio</strong> (<em>float</em><em>, </em><em>default: 0.1</em>) – The ratio of overlapping samples between guest and host, must between 0 and 1 exclusively</li>
<li><strong>guest_split_ratio</strong> (<em>float</em><em>, </em><em>default: 0.9</em>) – The ratio of number of samples excluding overlapping samples at guest side, must between 0 and 1 exclusively</li>
<li><strong>num_samples</strong> (<em>numeric</em><em>, </em><em>default: None</em>) – The total number of samples used for train/validation/test, must be positive integer or None. If None, all samples
would be used.</li>
<li><strong>balanced</strong> (<em>bool</em><em>, </em><em>default; True</em>) – The indicator indicating whether balance samples, must be bool</li>
<li><strong>is_read_table</strong> (<em>bool</em><em>, </em><em>default; False</em>) – The indicator indicating whether read data from dtable, must be bool</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.FTLModelParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">FTLModelParam</code><span class="sig-paren">(</span><em>max_iteration=10</em>, <em>batch_size=64</em>, <em>eps=1e-05</em>, <em>alpha=100</em>, <em>lr_decay=0.001</em>, <em>l2_para=1</em>, <em>is_encrypt=True</em>, <em>enc_ftl='dct_enc_ftl'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#FTLModelParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.FTLModelParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Defines parameters for FTL model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>max_iteration</strong> (<em>integer</em><em>, </em><em>default: 10</em>) – The number of passes over the training data (aka epochs), must be positive integer</li>
<li><strong>eps</strong> (<em>numeric</em><em>, </em><em>default: 1e-3</em>) – The converge threshold, must be positive number</li>
<li><strong>alpha</strong> (<em>numeric</em><em>, </em><em>default: 100</em>) – The weight for objective function loss, must be positive number</li>
<li><strong>is_encrypt</strong> (<em>bool</em><em>, </em><em>default; True</em>) – The indicator indicating whether we use encrypted version of ftl or plain version, must be bool</li>
<li><strong>enc_ftl</strong> (<em>str default &quot;dct_enc_ftl&quot;</em>) – The name for encrypted federated transfer learning algorithm</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.FTLValidDataParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">FTLValidDataParam</code><span class="sig-paren">(</span><em>file_path=None</em>, <em>num_samples=None</em>, <em>is_read_table=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#FTLValidDataParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.FTLValidDataParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Defines parameters for FTL validation data model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>file_path</strong> (<em>str</em><em>, </em><em>default: None</em>) – The file path to FTL data configuration JSON file, must be string or None</li>
<li><strong>num_samples</strong> (<em>numeric</em><em>, </em><em>default: None</em>) – The total number of samples used for validation, must be positive integer or None. If None, all samples
would be used.</li>
<li><strong>is_read_table</strong> (<em>bool</em><em>, </em><em>default; False</em>) – The indicator indicating whether read data from dtable, must be bool</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.FeatureBinningParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">FeatureBinningParam</code><span class="sig-paren">(</span><em>process_method='fit'</em>, <em>method='quantile'</em>, <em>compress_thres=10000</em>, <em>head_size=10000</em>, <em>error=0.001</em>, <em>bin_num=10</em>, <em>cols=-1</em>, <em>adjustment_factor=0.5</em>, <em>local_only=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#FeatureBinningParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.FeatureBinningParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the feature binning method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>process_method</strong> (<em>str</em><em>, </em><em>'fit'</em><em> or </em><em>'transform'</em><em>, </em><em>default: &quot;fit&quot;</em>) – Specify what process to do.</li>
<li><strong>method</strong> (<em>str</em><em>, </em><em>'quantile'</em><em>, </em><em>default: 'quantile'</em>) – Binning method.</li>
<li><strong>compress_thres</strong> (<em>int</em><em>, </em><em>default: 10000</em>) – When the number of saved summaries exceed this threshold, it will call its compress function</li>
<li><strong>head_size</strong> (<em>int</em><em>, </em><em>default: 10000</em>) – The buffer size to store inserted observations. When head list reach this buffer size, the
QuantileSummaries object start to generate summary(or stats) and insert into its sampled list.</li>
<li><strong>error</strong> (<em>float</em><em>, </em><em>0 &lt;= error &lt; 1 default: 0.001</em>) – The error of tolerance of binning. The final split point comes from original data, and the rank
of this value is close to the exact rank. More precisely,
floor((p - 2 * error) * N) &lt;= rank(x) &lt;= ceil((p + 2 * error) * N)
where p is the quantile in float, and N is total number of data.</li>
<li><strong>bin_num</strong> (<em>int</em><em>, </em><em>bin_num &gt; 0</em><em>, </em><em>default: 10</em>) – The max bin number for binning</li>
<li><strong>cols</strong> (<em>list of string</em><em> or </em><em>int</em><em>, </em><em>default: -1</em>) – Specify which columns need to calculated. -1 represent for all columns. If you need to indicate specific
cols, provide a list of header string instead of -1.</li>
<li><strong>adjustment_factor</strong> (<em>float</em><em>, </em><em>default: 0.5</em>) – the adjustment factor when calculating WOE. This is useful when there is no event or non-event in
a bin.</li>
<li><strong>local_only</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether just provide binning method to guest party. If true, host party will do nothing.</li>
<li><strong>display_result</strong> (<em>list</em><em>, </em><em>default:</em><em> [</em><em>'iv'</em><em>]</em>) – Specify what results to show. The available results include:
[‘iv’, ‘woe_array’, ‘iv_array’, ‘event_count_array’, ‘non_event_count_array’, ‘event_rate_array’,
‘non_event_rate_array’, ‘is_woe_monotonic’, ‘bin_nums’, ‘split_points’]
for each features</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.FeatureSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">FeatureSelectionParam</code><span class="sig-paren">(</span><em>method='fit'</em>, <em>select_cols=-1</em>, <em>filter_method=None</em>, <em>local_only=False</em>, <em>unique_param=&lt;federatedml.param.param.UniqueValueParam object&gt;</em>, <em>iv_value_param=&lt;federatedml.param.param.IVValueSelectionParam object&gt;</em>, <em>iv_percentile_param=&lt;federatedml.param.param.IVPercentileSelectionParam object&gt;</em>, <em>coe_param=&lt;federatedml.param.param.CoeffOfVarSelectionParam object&gt;</em>, <em>outlier_param=&lt;federatedml.param.param.OutlierColsSelectionParam object&gt;</em>, <em>bin_param=&lt;federatedml.param.param.FeatureBinningParam object&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#FeatureSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.FeatureSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the feature selection parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>'fit'</em><em>, </em><em>'transform'</em><em> or </em><em>'fit_transform'</em><em>, </em><em>default: 'fit'</em>) – <p>Decide what process to do.
fit_transform: fit select models and transfer data instance</p>
<p>transform: use fit models to transform data</p>
<p>fit:  fit the model only without transforming the data.</p>
</li>
<li><strong>select_cols</strong> (<em>list</em><em> or </em><em>int</em><em>, </em><em>default: -1</em>) – Specify which columns need to calculated. -1 represent for all columns.</li>
<li><strong>filter_method</strong> (<em>list</em><em>, </em><em>[</em><em>&quot;unique_value&quot;</em><em>, </em><em>&quot;iv_value_thres&quot;</em><em>, </em><em>&quot;iv_percentile&quot;</em><em>,</em>) – <blockquote>
<div><dl class="docutils">
<dt>“coefficient_of_variation_value_thres”, “outlier_cols”],</dt>
<dd>default: [“unique_value”, “iv_value_thres”,</dd>
</dl>
<p>”coefficient_of_variation_value_thres”, “outlier_cols”]</p>
</div></blockquote>
<p>Specify the filter methods used in feature selection. The orders of filter used is depended on this list.
Please be notified that, if a percentile method is used after some certain filter method,
the percentile represent for the ratio of rest features.</p>
<p>e.g. If you have 10 features at the beginning. After first filter method, you have 8 rest. Then, you want
top 80% highest iv feature. Here, we will choose floor(0.8 * 8) = 6 features instead of 8.</p>
<p>unique_value: filter the columns if all values in this feature is the same</p>
<dl class="docutils">
<dt>iv_value_thres: Use information value to filter columns. If this method is set, a float threshold need to be provided.</dt>
<dd>Filter those columns whose iv is smaller than threshold.</dd>
<dt>iv_percentile: Use information value to filter columns. If this method is set, a float ratio threshold</dt>
<dd>need to be provided. Pick floor(ratio * feature_num) features with higher iv. If multiple features around
the threshold are same, all those columns will be keep.</dd>
</dl>
<p>coefficient_of_variation_value_thres: Use coefficient of variation to judge whether filtered or not.</p>
<p>outlier_cols: Filter columns whose certain percentile value is larger than a threshold.</p>
<p>Note: iv_value_thres and iv_percentile should not exist at the same times</p>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.IVPercentileSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">IVPercentileSelectionParam</code><span class="sig-paren">(</span><em>percentile_threshold=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#IVPercentileSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.IVPercentileSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Use information values to select features.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>percentile_threshold</strong> (<em>float</em><em>, </em><em>0 &lt;= percentile_threshold &lt;= 1.0</em><em>, </em><em>default: 1.0</em>) – Percentile threshold for iv_percentile method</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.IVValueSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">IVValueSelectionParam</code><span class="sig-paren">(</span><em>value_threshold=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#IVValueSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.IVValueSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Use information values to select features.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>value_threshold</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Used if iv_value_thres method is used in feature selection.</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.InitParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">InitParam</code><span class="sig-paren">(</span><em>init_method='random_uniform'</em>, <em>init_const=1</em>, <em>fit_intercept=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#InitParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.InitParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Initialize Parameters used in initializing a model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>init_method</strong> (<em>str</em><em>, </em><em>'random_uniform'</em><em>, </em><em>'random_normal'</em><em>, </em><em>'ones'</em><em>, </em><em>'zeros'</em><em> or </em><em>'const'. default: 'random_uniform'</em>) – Initial method.</li>
<li><strong>init_const</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default: 1</em>) – Required when init_method is ‘const’. Specify the constant.</li>
<li><strong>fit_intercept</strong> (<em>bool</em><em>, </em><em>default: True</em>) – Whether to initialize the intercept or not.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.IntersectParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">IntersectParam</code><span class="sig-paren">(</span><em>intersect_method='raw'</em>, <em>random_bit=128</em>, <em>is_send_intersect_ids=True</em>, <em>is_get_intersect_ids=True</em>, <em>join_role='guest'</em>, <em>with_encode=False</em>, <em>encode_params=&lt;federatedml.param.param.EncodeParam object&gt;</em>, <em>only_output_key=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#IntersectParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.IntersectParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the intersect method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>intersect_method</strong> (<em>str</em><em>, </em><em>it supports 'rsa' and 'raw'</em><em>, </em><em>default by 'raw'</em>) – </li>
<li><strong>random_bit</strong> (<em>positive int</em><em>, </em><em>it will define the encrypt length of rsa algorithm. It effective only for intersect_method is rsa</em>) – </li>
<li><strong>is_send_intersect_ids</strong> (<em>bool. In rsa</em><em>, </em><em>'is_send_intersect_ids' is True means guest will send intersect results to host</em><em>, </em><em>and False will not.</em>) – while in raw, ‘is_send_intersect_ids’ is True means the role of “join_role” will send intersect results and the other will get them.
Default by True.</li>
<li><strong>is_get_intersect_ids</strong> (<em>bool</em><em>, </em><em>In rsa</em><em>, </em><em>it will get the results from other. It effective only for rsa and only be True will other's 'is_send_intersect_ids' is True.Default by True</em>) – </li>
<li><strong>join_role</strong> (<em>str</em><em>, </em><em>it supports &quot;guest&quot; and &quot;host&quot; only and effective only for raw. If it is &quot;guest&quot;</em><em>, </em><em>the host will send its ids to guest and find the intersection of</em>) – ids in guest; if it is “host”, the guest will send its ids. Default by “guest”.</li>
<li><strong>with_encode</strong> (<em>bool</em><em>, </em><em>if True</em><em>, </em><em>it will use encode method for intersect ids. It effective only for &quot;raw&quot;.</em>) – </li>
<li><strong>encode_params</strong> (<a class="reference internal" href="#federatedml.param.intersect_param.EncodeParam" title="federatedml.param.intersect_param.EncodeParam"><em>EncodeParam</em></a><em>, </em><em>it effective only for with_encode is True</em>) – </li>
<li><strong>only_output_key</strong> (<em>bool</em><em>, </em><em>if true</em><em>, </em><em>the results of intersection will include key and value which from input data; if false</em><em>, </em><em>it will just include key from input</em>) – data and the value will be empty or some useless character like “intersect_id”</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.LocalModelParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">LocalModelParam</code><span class="sig-paren">(</span><em>input_dim=None</em>, <em>encode_dim=5</em>, <em>learning_rate=0.001</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#LocalModelParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.LocalModelParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Defines parameters for FTL model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input_dim</strong> (<em>integer</em><em>, </em><em>default: None</em>) – The dimension of input samples, must be positive integer</li>
<li><strong>encode_dim</strong> (<em>integer</em><em>, </em><em>default: 5</em>) – The dimension of the encoded representation of input samples, must be positive integer</li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>default: 0.001</em>) – The learning rate for training model, must between 0 and 1 exclusively</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.LogisticParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">LogisticParam</code><span class="sig-paren">(</span><em>penalty='L2'</em>, <em>eps=1e-05</em>, <em>alpha=1.0</em>, <em>optimizer='sgd'</em>, <em>party_weight=1</em>, <em>batch_size=-1</em>, <em>learning_rate=0.01</em>, <em>init_param=&lt;federatedml.param.param.InitParam object&gt;</em>, <em>max_iter=100</em>, <em>converge_func='diff'</em>, <em>encrypt_param=&lt;federatedml.param.param.EncryptParam object&gt;</em>, <em>re_encrypt_batches=2</em>, <em>model_path='lr_model'</em>, <em>table_name='lr_table'</em>, <em>encrypted_mode_calculator_param=&lt;federatedml.param.param.EncryptedModeCalculatorParam object&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#LogisticParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.LogisticParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Parameters used for Logistic Regression both for Homo mode or Hetero mode.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>penalty</strong> (<em>str</em><em>, </em><em>'L1'</em><em> or </em><em>'L2'. default: 'L2'</em>) – Penalty method used in LR. Please note that, when using encrypted version in HomoLR,
‘L1’ is not supported.</li>
<li><strong>eps</strong> (<em>float</em><em>, </em><em>default: 1e-5</em>) – The tolerance of convergence</li>
<li><strong>alpha</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Regularization strength coefficient.</li>
<li><strong>optimizer</strong> (<em>str</em><em>, </em><em>'sgd'</em><em>, </em><em>'rmsprop'</em><em>, </em><em>'adam'</em><em> or </em><em>'adagrad'</em><em>, </em><em>default: 'sgd'</em>) – Optimize method</li>
<li><strong>party_weight</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default: 1</em>) – Required in Homo LR. Setting the weight of model updated for this party.
The higher weight set, the higher influence made for this party when updating model.</li>
<li><strong>batch_size</strong> (<em>int</em><em>, </em><em>default: -1</em>) – Batch size when updating model. -1 means use all data in a batch. i.e. Not to use mini-batch strategy.</li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>default: 0.01</em>) – Learning rate</li>
<li><strong>max_iter</strong> (<em>int</em><em>, </em><em>default: 100</em>) – The maximum iteration for training.</li>
<li><strong>converge_func</strong> (<em>str</em><em>, </em><em>'diff'</em><em> or </em><em>'abs'</em><em>, </em><em>default: 'diff'</em>) – <dl class="docutils">
<dt>Method used to judge converge or not.</dt>
<dd><ol class="first last loweralpha">
<li>diff： Use difference of loss between two iterations to judge whether converge.</li>
<li>abs: Use the absolute value of loss to judge whether converge. i.e. if loss &lt; eps, it is converged.</li>
</ol>
</dd>
</dl>
</li>
<li><strong>re_encrypt_batches</strong> (<em>int</em><em>, </em><em>default: 2</em>) – Required when using encrypted version HomoLR. Since multiple batch updating coefficient may cause
overflow error. The model need to be re-encrypt for every several batches. Please be careful when setting
this parameter. Too large batches may cause training failure.</li>
<li><strong>model_path</strong> (<em>Abandoned</em>) – </li>
<li><strong>table_name</strong> (<em>Abandoned</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.ObjectiveParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">ObjectiveParam</code><span class="sig-paren">(</span><em>objective=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#ObjectiveParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.ObjectiveParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define objective parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>objective</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>accepted None</em><em>,</em><em>'cross_entropy'</em><em>,</em><em>'lse'</em><em>,</em><em>'lae'</em><em>,</em><em>'log_cosh'</em><em>,</em><em>'tweedie'</em><em>,</em><em>'fair'</em><em>,</em><em>'huber' only</em><em>,</em>) – None in host’s config, should be str in guest’config.
when task_type is classification, only support cross_enctropy,
other 6 types support in regression task. default: None</li>
<li><strong>params</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>should be non empty list when objective is 'tweedie'</em><em>,</em><em>'fair'</em><em>,</em><em>'huber'</em><em>,</em>) – first element of list shoulf be a float-number large than 0.0 when objective is ‘fair’,’huber’,
first element of list should be a float-number in [1.0, 2.0) when objective is ‘tweedie’</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.OneHotEncoderParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">OneHotEncoderParam</code><span class="sig-paren">(</span><em>cols=-1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#OneHotEncoderParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.OneHotEncoderParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>cols</strong> (<em>list</em><em> or </em><em>int</em><em>, </em><em>default: -1</em>) – Specify which columns need to calculated. -1 represent for all columns.</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.OneVsRestParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">OneVsRestParam</code><span class="sig-paren">(</span><em>has_arbiter=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#OneVsRestParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.OneVsRestParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the one_vs_rest parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>has_arbiter</strong> (<em>bool. For some algorithm</em><em>, </em><em>may not has arbiter</em><em>, </em><em>for instances</em><em>, </em><em>secureboost of FATE</em><em>,  </em><em>for these algorithms</em><em>, </em><em>it should be set to false.</em>) – default true</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.OutlierColsSelectionParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">OutlierColsSelectionParam</code><span class="sig-paren">(</span><em>percentile=1.0</em>, <em>upper_threshold=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#OutlierColsSelectionParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.OutlierColsSelectionParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Given percentile and threshold. Judge if this quantile point is larger than threshold. Filter those larger ones.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>percentile</strong> (<em>float</em><em>, </em><em>[</em><em>0.</em><em>, </em><em>1.</em><em>] </em><em>default: 1.0</em>) – The percentile points to compare.</li>
<li><strong>upper_threshold</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Percentile threshold for coefficient_of_variation_percentile method</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.PredictParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">PredictParam</code><span class="sig-paren">(</span><em>with_proba=True</em>, <em>threshold=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#PredictParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.PredictParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the predict method of HomoLR, HeteroLR, SecureBoosting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>with_proba</strong> (<em>bool</em><em>, </em><em>Specify whether the result contains probability</em>) – </li>
<li><strong>threshold</strong> (<em>float</em><em> or </em><em>int</em><em>, </em><em>The threshold use to separate positive and negative class. Normally</em><em>, </em><em>it should be</em><em> (</em><em>0</em><em>,</em><em>1</em><em>)</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.SampleParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">SampleParam</code><span class="sig-paren">(</span><em>mode='random'</em>, <em>method='downsample'</em>, <em>fractions=None</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#SampleParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.SampleParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the sample method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>mode</strong> (<em>str</em><em>, </em><em>accepted 'random'</em><em>,</em><em>'stratified'' only in this version</em><em>, </em><em>specify samplet to use</em><em>, </em><em>default: 'random'</em>) – </li>
<li><strong>method</strong> (<em>str</em><em>, </em><em>accepted 'downsample'</em><em>,</em><em>'upsample' only in this version. default: 'downsample'</em>) – </li>
<li><strong>fractions</strong> (<em>None</em><em> or </em><em>float</em><em> or </em><em>list</em><em>, </em><em>if mode equals to random</em><em>, </em><em>it should be a float number greater than 0</em><em>, </em><em>otherwise a list of float elements. default: None</em>) – </li>
<li><strong>random_state</strong> (<em>int</em><em>, </em><em>RandomState instance</em><em> or </em><em>None</em><em>, </em><em>default: None</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.ScaleParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">ScaleParam</code><span class="sig-paren">(</span><em>method=None</em>, <em>mode='normal'</em>, <em>area='all'</em>, <em>feat_upper=None</em>, <em>feat_lower=None</em>, <em>out_upper=None</em>, <em>out_lower=None</em>, <em>with_mean=True</em>, <em>with_std=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#ScaleParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.ScaleParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define the feature scale parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>now it support &quot;min_max_scale&quot; and &quot;standard_scale&quot;</em><em>, </em><em>and will support other scale method soon.</em>) – Default None, which will do nothing for scale</li>
<li><strong>mode</strong> (<em>str</em><em>, </em><em>for method is &quot;min_max_scale&quot; and for &quot;standard_scale&quot; it is useless</em><em>, </em><em>the mode just support &quot;normal&quot; now</em><em>, </em><em>and will support &quot;cap&quot; mode in the furture.</em>) – for mode is “min_max_scale”, the feat_upper and feat_lower is the normal value and for “cap”, feat_upper and
feature_lower will between 0 and 1, which means the percentile of the column. Default “normal”</li>
<li><strong>area</strong> (<em>str</em><em>, </em><em>for method is &quot;min_max_scale&quot; and for &quot;standard_scale&quot; it is useless. It supports &quot;all&quot; and &quot;col&quot;. For &quot;all&quot;</em><em>,</em>) – feat_upper/feat_lower will act on all data column, so it will just be a value, and for “col”, it just acts
on one column they corresponding to, so feat_lower/feat_upper will be a list, which size will equal to the number of columns</li>
<li><strong>feat_upper</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>used for &quot;min_max_scale&quot;</em><em>, </em><em>the upper limit in the column. If the value is larger than feat_upper</em><em>, </em><em>it will be set to feat_upper. Default None.</em>) – </li>
<li><strong>feat_lower</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>used for &quot;min_max_scale&quot;</em><em>, </em><em>the lower limit in the column. If the value is less than feat_lower</em><em>, </em><em>it will be set to feat_lower. Default None.</em>) – </li>
<li><strong>out_upper</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>used for &quot;min_max_scale&quot;</em><em>, </em><em>The results of scale will be mapped to the area between out_lower and out_upper.Default None.</em>) – </li>
<li><strong>out_upper</strong> – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.UniqueValueParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">UniqueValueParam</code><span class="sig-paren">(</span><em>eps=1e-05</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#UniqueValueParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.UniqueValueParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Use the difference between max-value and min-value to judge.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>eps</strong> (<em>float</em><em>, </em><em>default: 1e-5</em>) – The column(s) will be filtered if its difference is smaller than eps.</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="federatedml.param.param.WorkFlowParam">
<em class="property">class </em><code class="descclassname">federatedml.param.param.</code><code class="descname">WorkFlowParam</code><span class="sig-paren">(</span><em>method='train'</em>, <em>train_input_table=None</em>, <em>train_input_namespace=None</em>, <em>model_table=None</em>, <em>model_namespace=None</em>, <em>predict_input_table=None</em>, <em>predict_input_namespace=None</em>, <em>predict_result_partition=1</em>, <em>predict_output_table=None</em>, <em>predict_output_namespace=None</em>, <em>evaluation_output_table=None</em>, <em>evaluation_output_namespace=None</em>, <em>data_input_table=None</em>, <em>data_input_namespace=None</em>, <em>intersect_data_output_table=None</em>, <em>intersect_data_output_namespace=None</em>, <em>dataio_param=&lt;federatedml.param.param.DataIOParam object&gt;</em>, <em>predict_param=&lt;federatedml.param.param.PredictParam object&gt;</em>, <em>evaluate_param=&lt;federatedml.param.param.EvaluateParam object&gt;</em>, <em>do_cross_validation=False</em>, <em>work_mode=0</em>, <em>n_splits=5</em>, <em>need_intersect=True</em>, <em>need_sample=False</em>, <em>need_feature_selection=False</em>, <em>need_scale=False</em>, <em>one_vs_rest=False</em>, <em>need_one_hot=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/param.html#WorkFlowParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.param.WorkFlowParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Define Workflow parameters used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>'train'</em><em>, </em><em>'predict'</em><em>, </em><em>'intersect'</em><em> or </em><em>'cross_validation'. default: 'train'</em>) – The working method of this task.</li>
<li><strong>train_input_table</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘train’. Specify the table name of input data in database.</li>
<li><strong>train_input_namespace</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘train’. Specify the namespace of input data in database.</li>
<li><strong>model_table</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘train’, ‘predict’ or ‘cross_validation’.
Specify the table name to save or load model. When method is ‘train’ or ‘cross_validation’, this parameter
is used to save model. When method is predict, it is used to load model.</li>
<li><strong>model_namespace</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘train’, ‘predict’ or ‘cross_validation’.
Specify the namespace to save or load model. When method is ‘train’ or ‘cross_validation’, this parameter
is used to save model. When method is predict, it is used to load model.</li>
<li><strong>predict_input_table</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘predict’. Specify the table name of predict input data.</li>
<li><strong>predict_input_namespace</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘predict’. Specify the namespace of predict input data in database.</li>
<li><strong>predict_result_partition</strong> (<em>int</em><em>, </em><em>default: 1</em>) – The partition number used for predict result.</li>
<li><strong>predict_output_table</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘predict’. Specify the table name of predict output data.</li>
<li><strong>predict_output_namespace</strong> (<em>str</em><em>, </em><em>default: None</em>) – Required when method is ‘predict’. Specify the namespace of predict output data in database.</li>
<li><strong>evaluation_output_table</strong> (<em>str</em><em>, </em><em>default: None</em>) – <dl class="docutils">
<dt>Required when method is ‘train’, ‘predict’ or ‘cross_validation’.</dt>
<dd>Specify the table name of evalation output data.</dd>
</dl>
</li>
<li><strong>evaluation_output_namespace</strong> (<em>str</em><em>, </em><em>default: None</em>) – <dl class="docutils">
<dt>Required when method is ‘train’, ‘predict’ or ‘cross_validation’.</dt>
<dd>Specify the namespace of predict output data in database.</dd>
</dl>
</li>
<li><strong>data_input_table</strong> (<em>str</em><em>, </em><em>defalut: None</em>) – Required when method is ‘cross_validation’. Specify the table name of input data.</li>
<li><strong>data_input_namespace</strong> (<em>str</em><em>, </em><em>defalut: None</em>) – Required when method is ‘cross_validation’. Specify the namespace of input data.</li>
<li><strong>intersect_data_output_table</strong> (<em>str</em><em>, </em><em>defalut: None</em>) – Required when method is ‘intersect’. Specify the table name of output data.</li>
<li><strong>intersect_data_output_namespace</strong> (<em>str</em><em>, </em><em>defalut: None</em>) – Required when method is ‘intersect’. Specify the namespace of output data.</li>
<li><strong>do_cross_validation</strong> (<em>Abandonded.</em>) – </li>
<li><strong>work_mode</strong> (<em>int</em><em>, </em><em>0</em><em> or </em><em>1. default: 0</em>) – Specify the work mode. 0 means standalone version, 1 represent for cluster version.</li>
<li><strong>n_splits</strong> (<em>int</em><em>, </em><em>default: 5</em>) – The number of fold used in KFold validation. It is required in ‘cross_validation’ only.</li>
<li><strong>need_intersect</strong> (<em>bool</em><em>, </em><em>default: True</em>) – Whether this task need to do intersect. No need to specify in Homo task.</li>
<li><strong>need_sample</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether this task need to do feature selection or not.</li>
<li><strong>need_feature_selection</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether this task need to do feature selection or not.</li>
<li><strong>need_one_hot</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether this task need to do one_hot encode</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="module-federatedml.param.predict_param">
<span id="federatedml-param-predict-param-module"></span><h2>federatedml.param.predict_param module<a class="headerlink" href="#module-federatedml.param.predict_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.predict_param.PredictParam">
<em class="property">class </em><code class="descclassname">federatedml.param.predict_param.</code><code class="descname">PredictParam</code><span class="sig-paren">(</span><em>with_proba=True</em>, <em>threshold=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/predict_param.html#PredictParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.predict_param.PredictParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the predict method of HomoLR, HeteroLR, SecureBoosting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>with_proba</strong> (<em>bool</em><em>, </em><em>Specify whether the result contains probability</em>) – </li>
<li><strong>threshold</strong> (<em>float</em><em> or </em><em>int</em><em>, </em><em>The threshold use to separate positive and negative class. Normally</em><em>, </em><em>it should be</em><em> (</em><em>0</em><em>,</em><em>1</em><em>)</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.predict_param.PredictParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/predict_param.html#PredictParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.predict_param.PredictParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.sample_param">
<span id="federatedml-param-sample-param-module"></span><h2>federatedml.param.sample_param module<a class="headerlink" href="#module-federatedml.param.sample_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.sample_param.SampleParam">
<em class="property">class </em><code class="descclassname">federatedml.param.sample_param.</code><code class="descname">SampleParam</code><span class="sig-paren">(</span><em>mode='random'</em>, <em>method='downsample'</em>, <em>fractions=None</em>, <em>random_state=None</em>, <em>task_type='hetero'</em>, <em>need_run=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/sample_param.html#SampleParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.sample_param.SampleParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the sample method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>mode</strong> (<em>str</em><em>, </em><em>accepted 'random'</em><em>,</em><em>'stratified'' only in this version</em><em>, </em><em>specify samplet to use</em><em>, </em><em>default: 'random'</em>) – </li>
<li><strong>method</strong> (<em>str</em><em>, </em><em>accepted 'downsample'</em><em>,</em><em>'upsample' only in this version. default: 'downsample'</em>) – </li>
<li><strong>fractions</strong> (<em>None</em><em> or </em><em>float</em><em> or </em><em>list</em><em>, </em><em>if mode equals to random</em><em>, </em><em>it should be a float number greater than 0</em><em>, </em><em>otherwise a list of float elements. default: None</em>) – </li>
<li><strong>random_state</strong> (<em>int</em><em>, </em><em>RandomState instance</em><em> or </em><em>None</em><em>, </em><em>default: None</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.sample_param.SampleParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/sample_param.html#SampleParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.sample_param.SampleParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param.scale_param">
<span id="federatedml-param-scale-param-module"></span><h2>federatedml.param.scale_param module<a class="headerlink" href="#module-federatedml.param.scale_param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.scale_param.ScaleParam">
<em class="property">class </em><code class="descclassname">federatedml.param.scale_param.</code><code class="descname">ScaleParam</code><span class="sig-paren">(</span><em>method=None</em>, <em>mode='normal'</em>, <em>area='all'</em>, <em>feat_upper=None</em>, <em>feat_lower=None</em>, <em>out_upper=None</em>, <em>out_lower=None</em>, <em>with_mean=True</em>, <em>with_std=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/scale_param.html#ScaleParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.scale_param.ScaleParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the feature scale parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>now it support &quot;min_max_scale&quot; and &quot;standard_scale&quot;</em><em>, </em><em>and will support other scale method soon.</em>) – Default None, which will do nothing for scale</li>
<li><strong>mode</strong> (<em>str</em><em>, </em><em>for method is &quot;min_max_scale&quot; and for &quot;standard_scale&quot; it is useless</em><em>, </em><em>the mode just support &quot;normal&quot; now</em><em>, </em><em>and will support &quot;cap&quot; mode in the furture.</em>) – for mode is “min_max_scale”, the feat_upper and feat_lower is the normal value and for “cap”, feat_upper and
feature_lower will between 0 and 1, which means the percentile of the column. Default “normal”</li>
<li><strong>area</strong> (<em>str</em><em>, </em><em>for method is &quot;min_max_scale&quot; and for &quot;standard_scale&quot; it is useless. It supports &quot;all&quot; and &quot;col&quot;. For &quot;all&quot;</em><em>,</em>) – feat_upper/feat_lower will act on all data column, so it will just be a value, and for “col”, it just acts
on one column they corresponding to, so feat_lower/feat_upper will be a list, which size will equal to the number of columns</li>
<li><strong>feat_upper</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>used for &quot;min_max_scale&quot;</em><em>, </em><em>the upper limit in the column. If the value is larger than feat_upper</em><em>, </em><em>it will be set to feat_upper. Default None.</em>) – </li>
<li><strong>feat_lower</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>used for &quot;min_max_scale&quot;</em><em>, </em><em>the lower limit in the column. If the value is less than feat_lower</em><em>, </em><em>it will be set to feat_lower. Default None.</em>) – </li>
<li><strong>out_upper</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>used for &quot;min_max_scale&quot;</em><em>, </em><em>The results of scale will be mapped to the area between out_lower and out_upper.Default None.</em>) – </li>
<li><strong>out_upper</strong> – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.scale_param.ScaleParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/scale_param.html#ScaleParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.scale_param.ScaleParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
<div class="section" id="module-federatedml.param">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-federatedml.param" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="federatedml.param.DataIOParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">DataIOParam</code><span class="sig-paren">(</span><em>input_format='dense'</em>, <em>delimitor='</em>, <em>'</em>, <em>data_type='float64'</em>, <em>tag_with_value=False</em>, <em>tag_value_delimitor=':'</em>, <em>missing_fill=True</em>, <em>default_value=0</em>, <em>missing_fill_method=None</em>, <em>missing_impute=None</em>, <em>outlier_replace=True</em>, <em>outlier_replace_method=None</em>, <em>outlier_impute=None</em>, <em>outlier_replace_value=0</em>, <em>with_label=False</em>, <em>label_idx=0</em>, <em>label_type='int'</em>, <em>output_format='dense'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/dataio_param.html#DataIOParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.DataIOParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define dataio parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input_format</strong> (<em>str</em><em>, </em><em>accepted 'dense'</em><em>,</em><em>'sparse' 'tag' only in this version. default: 'dense'</em>) – </li>
<li><strong>delimitor</strong> (<em>str</em><em>, </em><em>the delimitor of data input</em><em>, </em><em>default: '</em><em>,</em><em>'</em>) – </li>
<li><strong>data_type</strong> (<em>str</em><em>, </em><em>the data type of data input</em><em>, </em><em>accedted 'float'</em><em>,</em><em>'float64'</em><em>,</em><em>'int'</em><em>,</em><em>'int64'</em><em>,</em><em>'str'</em><em>,</em><em>'long'</em>) – “default: “float64”</li>
<li><strong>tag_with_value</strong> (<em>bool</em><em>, </em><em>use if input_format is 'tag'</em><em>, </em><em>if tag_with_value is True</em><em>, </em><em>input column data format should be tag</em><em>[</em><em>delimitor</em><em>]</em><em>value</em><em>, </em><em>otherwise is tag only</em>) – </li>
<li><strong>tag_value_delimitor</strong> (<em>str</em><em>, </em><em>use if input_format is 'tag' and 'tag_with_value' is True</em><em>, </em><em>delimitor of tag</em><em>[</em><em>delimitor</em><em>]</em><em>value column value.</em>) – </li>
<li><strong>missing_fill</strong> (<em>bool</em><em>, </em><em>need to fill missing value</em><em> or </em><em>not</em><em>, </em><em>accepted only True/False</em><em>, </em><em>default: True</em>) – </li>
<li><strong>default_value</strong> (<em>None</em><em> or </em><em>single object type</em><em> or </em><em>list</em><em>, </em><em>the value to replace missing value.</em>) – <p>if None, it will use default value define in federatedml/feature/imputer.py,
if single object, will fill missing value with this object,
if list, it’s length should be the sample of input data’ feature dimension,</p>
<blockquote>
<div>means that if some column happens to have missing values, it will replace it
the value by element in the identical position of this list.</div></blockquote>
<p>default: None</p>
</li>
<li><strong>missing_fill_method</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>the method to replace missing value</em><em>, </em><em>should be one of</em><em> [</em><em>None</em><em>, </em><em>'min'</em><em>, </em><em>'max'</em><em>, </em><em>'mean'</em><em>, </em><em>'designated'</em><em>]</em><em>, </em><em>default: None</em>) – </li>
<li><strong>missing_impute</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>element of list can be any type</em><em>, or </em><em>auto generated if value is None</em><em>, </em><em>define which values to be consider as missing</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_replace</strong> (<em>bool</em><em>, </em><em>need to replace outlier value</em><em> or </em><em>not</em><em>, </em><em>accepted only True/False</em><em>, </em><em>default: True</em>) – </li>
<li><strong>outlier_replace_method</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>the method to replace missing value</em><em>, </em><em>should be one of</em><em> [</em><em>None</em><em>, </em><em>'min'</em><em>, </em><em>'max'</em><em>, </em><em>'mean'</em><em>, </em><em>'designated'</em><em>]</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_impute</strong> (<em>None</em><em> or </em><em>list</em><em>,  </em><em>element of list can be any type</em><em>, </em><em>which values should be regard as missing value</em><em>, </em><em>default: None</em>) – </li>
<li><strong>outlier_replace_value</strong> (<em>None</em><em> or </em><em>single object type</em><em> or </em><em>list</em><em>, </em><em>the value to replace outlier.</em>) – <p>if None, it will use default value define in federatedml/feature/imputer.py,
if single object, will replace outlier with this object,
if list, it’s length should be the sample of input data’ feature dimension,</p>
<blockquote>
<div>means that if some column happens to have outliers, it will replace it
the value by element in the identical position of this list.</div></blockquote>
<p>default: None</p>
</li>
<li><strong>with_label</strong> (<em>bool</em><em>, </em><em>True if input data consist of label</em><em>, </em><em>False otherwise. default: 'false'</em>) – </li>
<li><strong>label_idx</strong> (<em>int</em><em>, </em><em>accepted 'int'</em><em>,</em><em>'long' only</em><em>, </em><em>use when with_label is True. default: 'false'</em>) – </li>
<li><strong>label_type</strong> (<em>object</em><em>, </em><em>accepted 'int'</em><em>,</em><em>'int64'</em><em>,</em><em>'float'</em><em>,</em><em>'float64'</em><em>,</em><em>'long'</em><em>,</em><em>'str' only</em><em>,</em>) – use when with_label is True. default: ‘false’</li>
<li><strong>output_format</strong> (<em>str</em><em>, </em><em>accepted 'dense'</em><em>,</em><em>'sparse' only in this version. default: 'dense'</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.DataIOParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/dataio_param.html#DataIOParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.DataIOParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.DecisionTreeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">DecisionTreeParam</code><span class="sig-paren">(</span><em>criterion_method='xgboost', criterion_params=[0.1], max_depth=5, min_sample_split=2, min_imputiry_split=0.001, min_leaf_node=1, max_split_nodes=65536, feature_importance_type='split', n_iter_no_change=True, tol=0.001</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#DecisionTreeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.DecisionTreeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define decision tree parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>criterion_method</strong> (<em>str</em><em>, </em><em>accepted &quot;xgboost&quot; only</em><em>, </em><em>the criterion function to use</em><em>, </em><em>default: 'xgboost'</em>) – </li>
<li><strong>criterion_params</strong> (<em>list</em><em>, </em><em>should be non empty and first element is float-number</em><em>, </em><em>default: 0.1.</em>) – </li>
<li><strong>max_depth</strong> (<em>int</em><em>, </em><em>positive integer</em><em>, </em><em>the max depth of a decision tree</em><em>, </em><em>default: 5</em>) – </li>
<li><strong>min_sample_split</strong> (<em>int</em><em>, </em><em>least quantity of nodes to split</em><em>, </em><em>default: 2</em>) – </li>
<li><strong>min_impurity_split</strong> (<em>float</em><em>, </em><em>least gain of a single split need to reach</em><em>, </em><em>default: 1e-3</em>) – </li>
<li><strong>min_leaf_node</strong> (<em>int</em><em>, </em><em>when samples no more than min_leaf_node</em><em>, </em><em>it becomes a leave</em><em>, </em><em>default: 1</em>) – </li>
<li><strong>max_split_nodes</strong> (<em>int</em><em>, </em><em>positive integer</em><em>, </em><em>we will use no more than max_split_nodes to</em>) – parallel finding their splits in a batch, for memory consideration. default is 65536</li>
<li><strong>n_iter_no_change</strong> (<em>bool</em><em>, </em><em>accepted True</em><em>,</em><em>False only</em><em>, </em><em>if set to True</em><em>, </em><em>tol will use to consider</em>) – stop tree growth. default: True</li>
<li><strong>feature_importance_type</strong> (<em>str</em><em>, </em><em>support 'split'</em><em>, </em><em>'gain' only.</em>) – if is ‘split’, feature_importances calculate by feature split times,
if is ‘gain’, feature_importances calculate by feature split gain.
default: ‘split’</li>
<li><strong>tol</strong> (<em>float</em><em>, </em><em>only use when n_iter_no_change is set to True</em><em>, </em><em>default: 0.001</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.DecisionTreeParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#DecisionTreeParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.DecisionTreeParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.InitParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">InitParam</code><span class="sig-paren">(</span><em>init_method='random_uniform'</em>, <em>init_const=1</em>, <em>fit_intercept=True</em>, <em>random_seed=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#InitParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.InitParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Initialize Parameters used in initializing a model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>init_method</strong> (<em>str</em><em>, </em><em>'random_uniform'</em><em>, </em><em>'random_normal'</em><em>, </em><em>'ones'</em><em>, </em><em>'zeros'</em><em> or </em><em>'const'. default: 'random_uniform'</em>) – Initial method.</li>
<li><strong>init_const</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default: 1</em>) – Required when init_method is ‘const’. Specify the constant.</li>
<li><strong>fit_intercept</strong> (<em>bool</em><em>, </em><em>default: True</em>) – Whether to initialize the intercept or not.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.InitParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#InitParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.InitParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.LogisticParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">LogisticParam</code><span class="sig-paren">(</span><em>penalty='L2'</em>, <em>eps=1e-05</em>, <em>alpha=1.0</em>, <em>optimizer='sgd'</em>, <em>party_weight=1</em>, <em>batch_size=-1</em>, <em>learning_rate=0.01</em>, <em>init_param=&lt;federatedml.param.logistic_regression_param.InitParam object&gt;</em>, <em>max_iter=100</em>, <em>converge_func='diff'</em>, <em>encrypt_param=&lt;federatedml.param.encrypt_param.EncryptParam object&gt;</em>, <em>re_encrypt_batches=2</em>, <em>encrypted_mode_calculator_param=&lt;federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object&gt;</em>, <em>need_run=True</em>, <em>predict_param=&lt;federatedml.param.predict_param.PredictParam object&gt;</em>, <em>cv_param=&lt;federatedml.param.cross_validation_param.CrossValidationParam object&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#LogisticParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.LogisticParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Parameters used for Logistic Regression both for Homo mode or Hetero mode.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>penalty</strong> (<em>str</em><em>, </em><em>'L1'</em><em> or </em><em>'L2'. default: 'L2'</em>) – Penalty method used in LR. Please note that, when using encrypted version in HomoLR,
‘L1’ is not supported.</li>
<li><strong>eps</strong> (<em>float</em><em>, </em><em>default: 1e-5</em>) – The tolerance of convergence</li>
<li><strong>alpha</strong> (<em>float</em><em>, </em><em>default: 1.0</em>) – Regularization strength coefficient.</li>
<li><strong>optimizer</strong> (<em>str</em><em>, </em><em>'sgd'</em><em>, </em><em>'rmsprop'</em><em>, </em><em>'adam'</em><em> or </em><em>'adagrad'</em><em>, </em><em>default: 'sgd'</em>) – Optimize method</li>
<li><strong>party_weight</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default: 1</em>) – Required in Homo LR. Setting the weight of model updated for this party.
The higher weight set, the higher influence made for this party when updating model.</li>
<li><strong>batch_size</strong> (<em>int</em><em>, </em><em>default: -1</em>) – Batch size when updating model. -1 means use all data in a batch. i.e. Not to use mini-batch strategy.</li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>default: 0.01</em>) – Learning rate</li>
<li><strong>max_iter</strong> (<em>int</em><em>, </em><em>default: 100</em>) – The maximum iteration for training.</li>
<li><strong>converge_func</strong> (<em>str</em><em>, </em><em>'diff'</em><em> or </em><em>'abs'</em><em>, </em><em>default: 'diff'</em>) – <dl class="docutils">
<dt>Method used to judge converge or not.</dt>
<dd><ol class="first last loweralpha">
<li>diff： Use difference of loss between two iterations to judge whether converge.</li>
<li>abs: Use the absolute value of loss to judge whether converge. i.e. if loss &lt; eps, it is converged.</li>
</ol>
</dd>
</dl>
</li>
<li><strong>re_encrypt_batches</strong> (<em>int</em><em>, </em><em>default: 2</em>) – Required when using encrypted version HomoLR. Since multiple batch updating coefficient may cause
overflow error. The model need to be re-encrypt for every several batches. Please be careful when setting
this parameter. Too large batches may cause training failure.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.LogisticParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/logistic_regression_param.html#LogisticParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.LogisticParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.ObjectiveParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">ObjectiveParam</code><span class="sig-paren">(</span><em>objective=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#ObjectiveParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ObjectiveParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define objective parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>objective</strong> (<em>None</em><em> or </em><em>str</em><em>, </em><em>accepted None</em><em>,</em><em>'cross_entropy'</em><em>,</em><em>'lse'</em><em>,</em><em>'lae'</em><em>,</em><em>'log_cosh'</em><em>,</em><em>'tweedie'</em><em>,</em><em>'fair'</em><em>,</em><em>'huber' only</em><em>,</em>) – None in host’s config, should be str in guest’config.
when task_type is classification, only support cross_enctropy,
other 6 types support in regression task. default: None</li>
<li><strong>params</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>should be non empty list when objective is 'tweedie'</em><em>,</em><em>'fair'</em><em>,</em><em>'huber'</em><em>,</em>) – first element of list shoulf be a float-number large than 0.0 when objective is ‘fair’,’huber’,
first element of list should be a float-number in [1.0, 2.0) when objective is ‘tweedie’</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.ObjectiveParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><em>task_type=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#ObjectiveParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.ObjectiveParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.EncryptParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">EncryptParam</code><span class="sig-paren">(</span><em>method='Paillier'</em>, <em>key_length=1024</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/encrypt_param.html#EncryptParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.EncryptParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define encryption method that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>default: 'Paillier'</em>) – If method is ‘Paillier’, Paillier encryption will be used for federated ml.
To use non-encryption version in HomoLR, just set this parameter to be any other str.
For detail of Paillier encryption, please check out the paper mentioned in README file.</li>
<li><strong>key_length</strong> (<em>int</em><em>, </em><em>default: 1024</em>) – Used to specify the length of key in this encryption method. Only needed when method is ‘Paillier’</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.EncryptParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/encrypt_param.html#EncryptParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.EncryptParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.BoostingTreeParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">BoostingTreeParam</code><span class="sig-paren">(</span><em>tree_param=&lt;federatedml.param.boosting_tree_param.DecisionTreeParam object&gt;</em>, <em>task_type='classification'</em>, <em>objective_param=&lt;federatedml.param.boosting_tree_param.ObjectiveParam object&gt;</em>, <em>learning_rate=0.3</em>, <em>num_trees=5</em>, <em>subsample_feature_rate=0.8</em>, <em>n_iter_no_change=True</em>, <em>tol=0.0001</em>, <em>encrypt_param=&lt;federatedml.param.encrypt_param.EncryptParam object&gt;</em>, <em>quantile_method='bin_by_sample_data'</em>, <em>bin_num=32</em>, <em>bin_gap=0.001</em>, <em>bin_sample_num=10000</em>, <em>encrypted_mode_calculator_param=&lt;federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object&gt;</em>, <em>predict_param=&lt;federatedml.param.predict_param.PredictParam object&gt;</em>, <em>cv_param=&lt;federatedml.param.cross_validation_param.CrossValidationParam object&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#BoostingTreeParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.BoostingTreeParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define boosting tree parameters that used in federated ml.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>task_type</strong> (<em>str</em><em>, </em><em>accepted 'classification'</em><em>, </em><em>'regression' only</em><em>, </em><em>default: 'classification'</em>) – </li>
<li><strong>tree_param</strong> (<em>DecisionTreeParam Object</em><em>, </em><em>default: DecisionTreeParam</em><em>(</em><em>)</em>) – </li>
<li><strong>objective_param</strong> (<em>ObjectiveParam Object</em><em>, </em><em>default: ObjectiveParam</em><em>(</em><em>)</em>) – </li>
<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>accepted float</em><em>, </em><em>int</em><em> or </em><em>long only</em><em>, </em><em>the learning rate of secure boost. default: 0.3</em>) – </li>
<li><strong>num_trees</strong> (<em>int</em><em>, </em><em>accepted int</em><em>, </em><em>float only</em><em>, </em><em>the max number of trees to build. default: 5</em>) – </li>
<li><strong>subsample_feature_rate</strong> (<em>float</em><em>, </em><em>a float-number in</em><em> [</em><em>0</em><em>, </em><em>1</em><em>]</em><em>, </em><em>default: 0.8</em>) – </li>
<li><strong>n_iter_no_change</strong> (<em>bool</em><em>,</em>) – when True and residual error less than tol, tree building process will stop. default: True</li>
<li><strong>encrypt_param</strong> (<em>EncodeParam Object</em><em>, </em><em>encrypt method use in secure boost</em><em>, </em><em>default: EncryptParam</em><em>(</em><em>)</em>) – </li>
<li><strong>quantile_method</strong> (<em>str</em><em>, </em><em>accepted 'bin_by_sample_data'</em><em> or </em><em>'bin_by_data_block' only</em><em>,</em>) – the quantile method use in secureboost, default: ‘bin_by_sample_data’</li>
<li><strong>bin_num</strong> (<em>int</em><em>, </em><em>positive integer greater than 1</em><em>, </em><em>bin number use in quantile. default: 32</em>) – </li>
<li><strong>bin_gap</strong> (<em>float</em><em>, </em><em>least difference between bin points</em><em>, </em><em>default: 1e-3</em>) – </li>
<li><strong>bin_sample_num</strong> (<em>int</em><em>, </em><em>if quantile method is 'bin_by_sample_data'</em><em>, </em><em>max amount of samples to find bins.</em>) – default: 10000</li>
<li><strong>encrypted_mode_calculator_param</strong> (<em>EncryptedModeCalculatorParam object</em><em>, </em><em>the calculation mode use in secureboost</em><em>,</em>) – default: EncryptedModeCalculatorParam()</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.BoostingTreeParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/boosting_tree_param.html#BoostingTreeParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.BoostingTreeParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.EvaluateParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">EvaluateParam</code><span class="sig-paren">(</span><em>eval_type='binary'</em>, <em>pos_label=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/evaluation_param.html#EvaluateParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.EvaluateParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the evaluation method of binary/multiple classification and regression</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>metrics</strong> (<em>A list of evaluate index. Support 'auc'</em><em>, </em><em>'ks'</em><em>, </em><em>'lift'</em><em>, </em><em>'precision'</em><em> ,</em><em>'recall' and 'accuracy'</em><em>, </em><em>'explain_variance'</em><em>,</em>) – ‘mean_absolute_error’, ‘mean_squared_error’, ‘mean_squared_log_error’,’median_absolute_error’,’r2_score’,’root_mean_squared_error’.
For example, metrics can be set as [‘auc’, ‘precision’, ‘recall’], then the results of these indexes will be output.</li>
<li><strong>eval_type</strong> (<em>string</em><em>, </em><em>support 'binary' for HomoLR</em><em>, </em><em>HeteroLR and Secureboosting. support 'regression' for Secureboosting. 'multi' is not support these version</em>) – </li>
<li><strong>pos_label</strong> (<em>specify positive label type</em><em>, </em><em>can be int</em><em>, </em><em>float and str</em><em>, </em><em>this depend on the data's label</em><em>, </em><em>this parameter effective only for 'binary'</em>) – </li>
<li><strong>thresholds</strong> (<em>A list of threshold. Specify the threshold use to separate positive and negative class. for example</em><em> [</em><em>0.1</em><em>, </em><em>0.3</em><em>,</em><em>0.5</em><em>]</em><em>, </em><em>this parameter effective only for 'binary'</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.EvaluateParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/evaluation_param.html#EvaluateParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.EvaluateParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.PredictParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">PredictParam</code><span class="sig-paren">(</span><em>with_proba=True</em>, <em>threshold=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/predict_param.html#PredictParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.PredictParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the predict method of HomoLR, HeteroLR, SecureBoosting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>with_proba</strong> (<em>bool</em><em>, </em><em>Specify whether the result contains probability</em>) – </li>
<li><strong>threshold</strong> (<em>float</em><em> or </em><em>int</em><em>, </em><em>The threshold use to separate positive and negative class. Normally</em><em>, </em><em>it should be</em><em> (</em><em>0</em><em>,</em><em>1</em><em>)</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.PredictParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/predict_param.html#PredictParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.PredictParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="class">
<dt id="federatedml.param.FeatureBinningParam">
<em class="property">class </em><code class="descclassname">federatedml.param.</code><code class="descname">FeatureBinningParam</code><span class="sig-paren">(</span><em>method='quantile'</em>, <em>compress_thres=10000</em>, <em>head_size=10000</em>, <em>error=0.001</em>, <em>bin_num=10</em>, <em>cols=-1</em>, <em>adjustment_factor=0.5</em>, <em>transform_param=&lt;federatedml.param.feature_binning_param.TransformParam object&gt;</em>, <em>local_only=False</em>, <em>need_run=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_binning_param.html#FeatureBinningParam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.FeatureBinningParam" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#federatedml.param.base_param.BaseParam" title="federatedml.param.base_param.BaseParam"><code class="xref py py-class docutils literal notranslate"><span class="pre">federatedml.param.base_param.BaseParam</span></code></a></p>
<p>Define the feature binning method</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>method</strong> (<em>str</em><em>, </em><em>'quantile'</em><em> or </em><em>'bucket'</em><em>, </em><em>default: 'quantile'</em>) – Binning method.</li>
<li><strong>compress_thres</strong> (<em>int</em><em>, </em><em>default: 10000</em>) – When the number of saved summaries exceed this threshold, it will call its compress function</li>
<li><strong>head_size</strong> (<em>int</em><em>, </em><em>default: 10000</em>) – The buffer size to store inserted observations. When head list reach this buffer size, the
QuantileSummaries object start to generate summary(or stats) and insert into its sampled list.</li>
<li><strong>error</strong> (<em>float</em><em>, </em><em>0 &lt;= error &lt; 1 default: 0.001</em>) – The error of tolerance of binning. The final split point comes from original data, and the rank
of this value is close to the exact rank. More precisely,
floor((p - 2 * error) * N) &lt;= rank(x) &lt;= ceil((p + 2 * error) * N)
where p is the quantile in float, and N is total number of data.</li>
<li><strong>bin_num</strong> (<em>int</em><em>, </em><em>bin_num &gt; 0</em><em>, </em><em>default: 10</em>) – The max bin number for binning</li>
<li><strong>cols</strong> (<em>list of int</em><em> or </em><em>int</em><em>, </em><em>default: -1</em>) – Specify which columns need to calculated. -1 represent for all columns. If you need to indicate specific
cols, provide a list of header index instead of -1.</li>
<li><strong>adjustment_factor</strong> (<em>float</em><em>, </em><em>default: 0.5</em>) – the adjustment factor when calculating WOE. This is useful when there is no event or non-event in
a bin.</li>
<li><strong>local_only</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether just provide binning method to guest party. If true, host party will do nothing.</li>
<li><strong>transform_param</strong> (<a class="reference internal" href="#federatedml.param.feature_binning_param.TransformParam" title="federatedml.param.feature_binning_param.TransformParam"><em>TransformParam</em></a>) – Define how to transfer the binned data.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="federatedml.param.FeatureBinningParam.check">
<code class="descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/federatedml/param/feature_binning_param.html#FeatureBinningParam.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#federatedml.param.FeatureBinningParam.check" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

</div>
</div>


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